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WO2012011350A1 - Gait posture assessment device - Google Patents

Gait posture assessment device Download PDF

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Publication number
WO2012011350A1
WO2012011350A1 PCT/JP2011/064130 JP2011064130W WO2012011350A1 WO 2012011350 A1 WO2012011350 A1 WO 2012011350A1 JP 2011064130 W JP2011064130 W JP 2011064130W WO 2012011350 A1 WO2012011350 A1 WO 2012011350A1
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WO
WIPO (PCT)
Prior art keywords
feature point
feature
factor
walking posture
locus
Prior art date
Application number
PCT/JP2011/064130
Other languages
French (fr)
Japanese (ja)
Inventor
森 健太郎
佐藤 哲也
Original Assignee
オムロンヘルスケア株式会社
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Filing date
Publication date
Application filed by オムロンヘルスケア株式会社 filed Critical オムロンヘルスケア株式会社
Publication of WO2012011350A1 publication Critical patent/WO2012011350A1/en

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Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116Determining posture transitions
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/112Gait analysis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813Specially adapted to be attached to a specific body part
    • A61B5/6823Trunk, e.g., chest, back, abdomen, hip

Definitions

  • the present invention relates to a walking posture determination device, and more particularly to a walking posture determination device suitable for determining a walking posture of a user who wears the device at a predetermined site.
  • the present invention has been made to solve the above-described problems, and one of its purposes is to provide a walking posture determination apparatus capable of evaluating a detailed walking posture with higher accuracy.
  • Another object of the present invention is to provide a walking posture determination device capable of displaying the walking posture in an easily understandable manner.
  • a walking posture determination device includes a main body, an acceleration sensor for detecting acceleration of the main body, and a controller. It is an apparatus for determining the walking posture of a user wearing a predetermined part.
  • a three-dimensional trajectory from which a moving component in the traveling direction during walking of a predetermined part to which the main body is attached has a pattern.
  • the pattern includes a plurality of feature points that define the features of the pattern.
  • the control unit Based on the acceleration detected by the acceleration sensor, the control unit removes the moving component in the traveling direction on the plane perpendicular to each of the three orthogonal directions of the vertical direction, the traveling direction, and the left-right direction.
  • a specifying unit that specifies the position of the first position, a first calculation unit that calculates a value of the characteristic factor of the trajectory based on the position specified by the specifying unit, a value of the characteristic factor and an index value indicating the walking posture
  • a second calculation unit that calculates an index value based on a characteristic factor value calculated by the first calculation unit according to a correlation obtained in advance, and an index value calculated by the second calculation unit
  • a determination unit for determining a walking posture based on
  • the walking posture determination device further includes a display unit.
  • the control unit further includes a display control unit that causes the display unit to display the walking posture determined by the determination unit and the target walking posture in a comparable manner.
  • the walking posture determination device further includes a display unit.
  • the control unit further includes a display control unit that causes the display unit to display advice for improving the walking posture determined by the determination unit.
  • the correlation is represented by a multiple regression equation that is a relational expression between a characteristic factor value as an objective variable and an index value as an explanatory variable, obtained by multiple regression analysis.
  • the feature points are a first feature point when the first foot is grounded and a second feature when the locus reaches the highest position while standing on the first foot. And a third feature point when the second foot comes in contact with the ground, and a fourth feature point when the locus reaches the highest position while standing on the second foot.
  • the feature factor is a first feature factor that is the distance in the vertical direction between the first feature point and the second feature point in the locus projected on the plane perpendicular to the traveling direction, and the feature factor is projected onto the plane perpendicular to the left-right direction.
  • the second feature factor calculated from the distance between the first feature point and the second feature point in the locus and the distance between the third feature point and the fourth feature point is included.
  • the index includes stride.
  • the multiple regression equation includes the product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, and the product of the second partial regression coefficient and the second feature factor obtained by the multiple regression analysis. , Is a formula for calculating the sum with the third partial regression coefficient.
  • the feature point is a first feature point when the first foot is grounded, a second feature point when the locus reaches the highest position while standing on the first foot, The third feature point on the rightmost side of the locus, the fourth feature point on the leftmost side of the locus, the fifth feature point on the most front side on the right side of the locus, and the sixth feature on the leftmost side of the locus.
  • the feature factor is the distance in the horizontal direction between the third feature point and the fourth feature point in the vertical direction between the first feature point and the second feature point in the trajectory projected on the plane perpendicular to the traveling direction. And the distance in the left-right direction between the fifth feature point and the sixth feature point in the trajectory projected on the surface perpendicular to the vertical direction, and the seventh feature point and the A second feature factor that is a quotient divided by the distance in the left-right direction from the eight feature points.
  • the index includes the step interval.
  • the multiple regression equation includes the product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, and the product of the second partial regression coefficient and the second feature factor obtained by the multiple regression analysis. , Is a formula for calculating the sum with the third partial regression coefficient.
  • the three-dimensional trajectory from which the moving component in the advancing direction during walking of the predetermined part to which the main body is attached has a pattern, and the pattern is a feature point that defines the feature of the pattern.
  • the traveling direction, and the left-right direction based on the acceleration detected by the acceleration sensor
  • the position of the point is specified, the value of the trait feature factor is calculated based on the specified position, and the value of the feature factor and the index value indicating the walking posture are calculated based on the calculated feature factor value.
  • the index value is calculated according to the correlation obtained in advance, and the walking posture is determined based on the calculated index value.
  • the walking posture determination device measures not only the number of steps, but also the amount of activity (also referred to as the amount of exercise) in exercise and daily activities (for example, vacuuming, carrying light luggage, cooking, etc.).
  • the embodiment will be described as an activity meter capable of However, the present invention is not limited to this, and the walking posture determination device may be a pedometer capable of measuring the number of steps.
  • FIG. 1 is an external view of an activity meter 100 according to the embodiment of the present invention.
  • the activity meter 100 is mainly composed of a main body portion 191 and a clip portion 192.
  • the clip unit 192 is used to fix the activity meter 100 to a user's clothes or the like.
  • the main body 191 includes a display change / decision switch 131, a left operation / memory switch 132, a right operation switch 133, and a part of a display unit 140, which will be described later.
  • a display 141 is provided.
  • display 141 is configured by a liquid crystal display (LCD), but is not limited thereto, and may be another type of display such as an EL (ElectroLuminescence) display. .
  • LCD liquid crystal display
  • EL ElectroLuminescence
  • FIG. 2 is a diagram showing a usage state of the activity meter 100 in this embodiment.
  • activity meter 100 is attached to a belt on a user's waist using clip portion 192, for example. In this embodiment, it is desirable that the activity meter 100 is fixedly mounted near the user's waist.
  • the user's traveling direction during walking is the Z axis (the forward direction is the positive direction), the user's left and right direction during walking is the X axis (the right direction is the positive direction), and the vertical direction is the Y axis.
  • a coordinate system with (vertically upward as a positive direction) is used.
  • FIG. 3 is a diagram showing a first example in which the hip locus during walking of the user is viewed from the direction of walking.
  • FIG. 4 is a diagram illustrating a second example in which the hip locus during walking of the user is viewed from the walking direction.
  • FIG. 3A and FIG. 4A are diagrams in which the hip locus during walking is superimposed on the user's image.
  • FIG. 3B and FIG. 4B are graphs showing the locus of the waist when the user walks.
  • this locus is a locus projected on the XY plane, which is a plane perpendicular to the Z-axis, during walking.
  • the right foot usually touches the ground after reaching the highest position after the right foot is released from the ground, and then the left foot is moved to the highest position after the left foot is released from the ground. After reaching, the foot is moved in the process of the left foot touching the ground.
  • the locus of the user's waist first moves from the lower right to the upper left, reaches the highest position on the upper left, then moves to the lower left, reaches the lowest position on the lower left, and then moves to the upper right. It is a specific pattern in which, after reaching the highest position on the upper right, heading to the lower right and reaching the lowest position on the lower right.
  • FIG. 5 is a diagram showing a plurality of examples in which the hip locus during walking of the user is viewed from the direction of walking.
  • FIG. 5 (A) is a diagram similar to the diagram shown in FIG. 3 (B).
  • FIG. 5A shows the locus of the waist when the user walks in the normal walking posture.
  • FIG. 5B is a diagram similar to the diagram illustrated in FIG.
  • FIG. 5B shows the locus of the waist when the user walks in a case where the step is wider than in the case of FIG.
  • FIG. 5C shows the locus of the waist when the user walks when the step is narrower than in the case of FIG.
  • FIG. 5 (D) shows the locus of the waist when the user walks when walking with a skate rather than the case of FIG. 5 (A).
  • FIG. 5 (E) shows the locus of the waist when the user walks when walking with the back of his back, rather than in the case of FIG. 5 (A).
  • FIG. 5 (F) shows the locus of the user's waist when walking on a forehead than in the case of FIG. 5 (A).
  • FIG. 5A to FIG. 5F look different, they have a specific pattern as described in FIG. 3 and FIG.
  • FIG. 6 is a diagram showing the correlation between the user's waist trajectory calculated from acceleration data and the measured user's waist trajectory calculated from the acceleration data in this embodiment.
  • FIG. 6 (A) is a diagram of an actually measured waist trajectory of the user when viewed from the direction of walking.
  • FIG. 6A is a view similar to FIG. 3B, FIG. 4B, and FIG. 5A to FIG. 5F.
  • the locus shown in FIG. 6A is obtained by, for example, photographing a place where the user is walking from the direction of travel with a camera and connecting the movements of a certain point near the waist by image processing. .
  • FIG. 6 (B) is a view of the hip locus during walking of the user, calculated from the acceleration data, as seen from the direction of walking.
  • a method of calculating the hip locus during walking of the user based on the triaxial acceleration data detected by the acceleration sensor of the activity meter 100 will be described. This locus is calculated by the control unit of the activity meter 100.
  • the accelerations Ax (t), Ay (t), and Az (t) in the X-axis, Y-axis, and Z-axis directions described with reference to FIG. 2 are specified.
  • the detected values obtained by the acceleration sensor are directly used as the accelerations Ax in the X-axis, Y-axis, and Z-axis directions.
  • T), Ay (t), Az (t) may be used.
  • the X-axis, Y-axis, and Z-axis directions are converted by coordinate conversion of the detection values obtained by the acceleration sensor.
  • the respective accelerations Ax (t), Ay (t), Az (t) are calculated.
  • the speed excluding the average speed component in the short time during the time of ⁇ 1 step that is, the relative speed Vx ′ (t), Vy ′ (t) and Vz ′ (t) are calculated.
  • the time for one step is T seconds, and T is calculated, for example, by calculating the time between acceleration peaks for each step.
  • the points (X (t) and Y (t)) having the calculated positions X (t) and Y (t) as X and Y coordinate values are plotted on the XY plane while changing t.
  • a trajectory obtained by projecting the trajectory of the user on the XY plane can be obtained.
  • An example of this locus is the locus shown in FIG.
  • These trajectories are traces of patterns as shown in FIGS. 7A to 9A, which will be described later.
  • FIG. 7 is a diagram for explaining the feature points included in the locus pattern projected on the XY plane in this embodiment.
  • FIG. 8 is a diagram for explaining the feature points included in the locus pattern projected on the XZ plane in this embodiment.
  • FIG. 9 is a diagram for explaining the feature points included in the locus pattern projected on the YZ plane in this embodiment.
  • feature point (1) is a point when the right foot touches down in the walking cycle.
  • the condition for specifying the feature point (1) is that the right side is the right and the top and the bottom is the lowest.
  • Feature point (2) is a point when the right foot is standing in the walking cycle (particularly a point when the user's waist is at the highest position in the vertical direction).
  • the condition for specifying the feature point (2) is a condition that it is after the feature point (1) and is the highest in the vertical direction.
  • Feature point (3) is the point when the left foot touches down in the walking cycle.
  • the condition for specifying the feature point (3) is a condition that it is after the feature point (2) and is the lowest in the vertical direction.
  • Feature point (4) is a point when the left foot is standing in the walking cycle (particularly a point when the user's waist is at the highest position in the vertical direction).
  • the condition for specifying the feature point (4) is a condition that it is after the feature point (3) and is the highest in the vertical direction.
  • Feature point (5) is the point when the right foot touches down in the walking cycle.
  • the condition for specifying the feature point (5) is the condition that it is after the feature point (4) and is the lowest in the vertical direction.
  • This feature point (5) is the feature point (1) of the next one cycle.
  • Feature point (6) is a point when the user's waist is on the rightmost side in the walking cycle.
  • the condition for specifying the feature point (6) is a condition that the value of X (t) calculated by Expression 7 is maximum when X (t) ⁇ 0 in one cycle.
  • Feature point (7) is a point when the user's waist is on the leftmost side in the walking cycle.
  • the condition for specifying the feature point (7) is a condition that the value of X (t) calculated by Expression 7 is minimum in one cycle when X (t) ⁇ 0.
  • Feature point (8) is the intersection of the loci of the waist in one walking cycle in the walking cycle.
  • the conditions for specifying the feature point (8) are the XY of the waist locus from the feature point (2) to the feature point (3) and the waist locus from the feature point (4) to the feature point (5). It is a condition that it is an intersection on a plane.
  • the feature point (9) is a point when the right foot touches down in the walking cycle.
  • the condition for specifying the feature point (9) is a condition that the right and left sides are right and the front and rear sides are the rearmost.
  • the feature point (10) is a point when the right foot is standing in the walking cycle (particularly a point when the user's waist is at the foremost position relative to the average position in a short time in the traveling direction).
  • the condition for specifying the feature point (10) is a condition that it is after the feature point (9) and is the foremost in terms of front and rear.
  • Feature point (11) is the point when the left foot touches down in the walking cycle.
  • the condition for specifying the feature point (11) is a condition that it is after the feature point (10) and is at the back of the front and rear.
  • the feature point (12) is a point when the left foot is standing in the walking cycle (particularly a point when the user's waist is at the forefront relative position to the average position in a short time in the traveling direction).
  • the condition for specifying the feature point (12) is a condition that it is after the feature point (11) and is the foremost in terms of front and rear.
  • Feature point (13) is the point when the right foot touches down in the walking cycle.
  • the condition for specifying the feature point (11) is a condition that it is after the feature point (12) and is at the back of the front and rear.
  • This feature point (13) is the feature point (9) of the next one cycle.
  • Feature point (14) is the intersection of the loci of the waist in one walking cycle in the walking cycle.
  • the conditions for specifying the feature point (14) are the XY of the waist locus from the feature point (10) to the feature point (11) and the waist locus from the feature point (12) to the feature point (13). It is a condition that it is an intersection on a plane.
  • feature points (1), (3), and (5) described in FIG. 7 are the lowest points in the locus pattern projected on the YZ plane.
  • the feature points (2) and (4) are the uppermost points in the locus pattern projected on the YZ plane.
  • FIG. 10 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the XY plane in this embodiment.
  • FIG. 11 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the XZ plane in this embodiment.
  • FIG. 12 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the YZ plane in this embodiment.
  • the feature factor Wu is a distance in the X-axis direction (referred to as “upper left / right width”) between the feature point (2) and the feature point (4) on the XY plane, and the feature point (2 ) Is subtracted from the X coordinate value of the feature point (4).
  • the feature factor Wd is a distance in the X-axis direction between the feature point (1) and the feature point (3) on the XY plane (referred to as “lower left / right width”), and the value of the X coordinate of the feature point (1). Is calculated by subtracting the X-coordinate value of the feature point (3).
  • the feature factor W is a distance in the X-axis direction between the feature point (6) and the feature point (7) on the XY plane (referred to as “left-right width”). It is calculated by subtracting the X coordinate value of the point (7).
  • the feature factor H1 is a distance in the Y-axis direction between the feature point (4) and the feature point (3) on the XY plane (referred to as “left-side vertical width”), and is obtained from the value of the Y coordinate of the feature point (4). It is calculated by subtracting the Y coordinate value of the feature point (3).
  • the feature factor Hr is a distance in the Y-axis direction between the feature point (2) and the feature point (1) on the XY plane (referred to as “right upper and lower width”), and is obtained from the value of the Y coordinate of the feature point (2). It is calculated by subtracting the Y coordinate value of the feature point (1).
  • Feature factor H is the average of feature factor H1 and feature factor Hr on the XY plane (referred to as “vertical width”), and is calculated by adding H1 and Hr and dividing by two.
  • the feature factor Hcl is the height (“left cross point height) of the feature point (8) on the basis of the feature point (3) on the XY plane.
  • the feature point (8) is obtained from the Y coordinate value of the feature point (8). It is calculated by subtracting the Y coordinate value of 3).
  • the feature factor Hcr is the height of the feature point (8) with respect to the feature point (1) on the XY plane (“right cross point height), and the feature point (8) is obtained from the Y coordinate value of the feature point (8). It is calculated by subtracting the Y coordinate value of 1).
  • the feature factor ISO is the height (referred to as “phase”) of the feature point (8) with respect to the vertical width of the trajectory in the XY plane.
  • the feature factor Hcl is divided by the feature factor Hl, and the feature factor Hcr is the feature factor. Calculated by adding to Hr and dividing by 2.
  • the feature factor Vlev is the degree of whether the upper side of the trajectory in the XY plane is open or lower (called “shape ⁇ or ⁇ ”), and is calculated by dividing the feature factor Wu by the feature factor Wd.
  • the feature factor Ilev is a factor (referred to as “shape I”) for specifying whether the shape of the locus on the XY plane is a vertically long shape or a horizontally long shape. Calculated by dividing.
  • the characteristic factor Hb is a ratio of the left and right vertical widths in the XY plane (referred to as “left / right vertical width ratio”), and is calculated by dividing the characteristic factor Hr by the characteristic factor Hl.
  • the feature factor Yb is the ratio of the left and right heights on the XY plane (referred to as “left / right height ratio”), and the difference between the Y coordinate value of the feature point (4) and the Y coordinate value of the feature point (1). Is divided by the difference between the Y coordinate value of the feature point (2) and the Y coordinate value of the feature point (3).
  • the feature factor Wb is a ratio of the left and right widths on the XY plane (referred to as “right / left width ratio”), and the difference between the X coordinate value of the feature point (6) and the X coordinate value of the feature point (8). , By dividing by the difference between the X coordinate value of the feature point (8) and the Y coordinate value of the feature point (7).
  • the characteristic factor St1 is the sum of the vertical amplitudes (referred to as “vertical amplitude from the right foot grounding to the left foot grounding”) until the left foot touches the ground on the XY plane. This is calculated by adding the value obtained by subtracting the Y coordinate value of the feature point (1) and the value obtained by subtracting the Y coordinate value of the feature point (3) from the Y coordinate value of the feature point (2).
  • the characteristic factor Str is a sum of vertical amplitudes from the left foot to the ground on the XY plane until the right foot contacts the ground (referred to as “vertical amplitude from the left foot grounding to the right foot grounding”). Calculated by adding the value obtained by subtracting the Y coordinate value of the feature point (3) and the value obtained by subtracting the Y coordinate value of the feature point (5) from the Y coordinate value of the feature point (4).
  • the feature factor “jun” is a factor (referred to as “writing order”) indicating whether the locus is written clockwise or counterclockwise, and the X coordinate of the feature point (2) and the feature point (4) It is calculated by making a positive / negative determination.
  • the feature factor WuSu is a distance in the X-axis direction (referred to as “upper left-right width”) between the feature point (10) and the feature point (12) on the XZ plane, and the feature point (10 ) Is subtracted from the X coordinate value of the feature point (12).
  • the feature factor WdSu is the distance in the X-axis direction between the feature point (9) and the feature point (11) on the XZ plane (referred to as “lower left / right width”), and the value of the X coordinate of the feature point (9) Is calculated by subtracting the X-coordinate value of the feature point (11).
  • the feature factor Wsu is a distance in the X-axis direction (referred to as “horizontal width”) between the feature point (6) and the feature point (7) on the XZ plane.
  • the feature factor Wsu is a feature based on the X coordinate value of the feature point (6). It is calculated by subtracting the X coordinate value of the point (7).
  • the feature factor HlSu is a distance in the Z-axis direction between the feature point (12) and the feature point (11) in the XZ plane (referred to as “left-side vertical width”), and is obtained from the value of the Z coordinate of the feature point (12). It is calculated by subtracting the Z coordinate value of the feature point (11).
  • the feature factor HrSu is a distance in the Z-axis direction between the feature point (10) and the feature point (9) in the XZ plane (referred to as “right upper and lower width”), and is obtained from the value of the Z coordinate of the feature point (10). It is calculated by subtracting the Z coordinate value of the feature point (9).
  • the feature factor Hsu is an average of the feature factor HlSu and the feature factor HrSu on the XZ plane (referred to as “vertical width”), and is calculated by adding HlSu and HrSu and dividing by two.
  • the feature factor HclSu is the height (“left cross point height) of the feature point (8) on the basis of the feature point (11) in the XZ plane.
  • the feature point (8) is obtained from the Z-coordinate value of the feature point (8). 11) is calculated by subtracting the value of the Z coordinate.
  • the feature factor HcrSu is the height (“right cross point height) of the feature point (8) on the basis of the feature point (9) in the XZ plane.
  • the feature point (8) is obtained from the Z coordinate value of the feature point (8) ( It is calculated by subtracting the Z coordinate value of 9).
  • the feature factor ISOSu is the height (referred to as “phase”) of the feature point (14) with respect to the vertical width of the trajectory on the XY plane, and is the same value as the ISO on the XY plane described in FIG.
  • the feature factor VlevSu is the degree of whether the upper side of the trajectory in the XZ plane is open or lower (referred to as “shape ⁇ or ⁇ ”), and is calculated by dividing the feature factor WuSu by the feature factor WdSu.
  • the feature factor IlevSu is a factor (referred to as “shape I”) for specifying whether the shape of the trajectory in the XZ plane is a vertically long shape or a horizontally long shape.
  • the feature factor Hsu is a feature factor Wsu. Calculated by dividing.
  • the characteristic factor HbSu is a ratio of the left and right vertical widths in the XZ plane (referred to as “left / right vertical width ratio”), and is calculated by dividing the characteristic factor HrSu by the characteristic factor HlSu.
  • the feature factor YbSu is the ratio of the left and right heights in the XZ plane (referred to as “left-right height ratio”), and the difference between the Z coordinate value of the feature point (13) and the Z coordinate value of the feature point (9). Is divided by the difference between the Z coordinate value of the feature point (10) and the Z coordinate value of the feature point (11).
  • the characteristic factor WbSu is a ratio of the left and right widths in the XZ plane (referred to as “right / left width ratio”), and is the same value as Wb of the XY plane described in FIG.
  • the characteristic factor StlSu is the sum of the front and rear amplitudes (referred to as “front and rear amplitudes from the right foot grounding to the left foot grounding”) until the left foot touches the ground in the XZ plane, and is the Z coordinate of the feature point (10). It is calculated by adding the value obtained by subtracting the Z coordinate value of the feature point (9) and the value obtained by subtracting the Z coordinate value of the feature point (11) from the Z coordinate value of the feature point (10).
  • the characteristic factor StrSu is the sum of the front and rear amplitudes (referred to as “front and rear amplitudes from the left foot contact to the right foot contact”) until the right foot contacts the ground in the XZ plane, and the Z coordinate of the feature point (12) It is calculated by adding the value obtained by subtracting the Z coordinate value of the feature point (11) and the value obtained by subtracting the Z coordinate value of the feature point (13) from the Z coordinate value of the feature point (12).
  • the characteristic factor Zfl is a width in which the left foot in the XZ plane is standing and moves back and forth after the position of the waist reaches the highest point (referred to as “backward movement of the waist from the uppermost point of the left stepped leg”). It is calculated by subtracting the Z coordinate value of the feature point (4) from the Z coordinate value of 12).
  • the characteristic factor Zfr is a width (referred to as “backward movement of the waist from the uppermost point of the right foot stand”) when the right foot is standing on the XZ plane and the position of the waist reaches the uppermost point and then moved back and forth. It is calculated by subtracting the Z coordinate value of the feature point (2) from the Z coordinate value of 10).
  • the characteristic factor Zf is a width that moves back and forth after the position of the waist reaches the highest point in the stance on the XZ plane (referred to as “backward movement of the waist from the highest point of the stance”), and the characteristic factor Zfl and the characteristic factor Calculated by adding Zfr and dividing by 2.
  • the characteristic factor Zbl is a width (referred to as “backward movement of the waist from the left foot contact”) after the left foot touches the ground in the XZ plane (referred to as “backward movement of the waist from the left foot contact”). It is calculated by subtracting the Z coordinate value of the feature point (3).
  • the feature factor Zbr is a width (referred to as “backward back-and-forth movement from the right foot grounding”) after the right foot in the XZ plane touches the ground, and from the value of the Z coordinate of the feature point (9). It is calculated by subtracting the Z coordinate value of the feature point (5).
  • the feature factor Zb is a width in which the position of the waist has moved back and forth after the foot in the XZ plane contacts the ground (referred to as “backward movement of the waist from the ground”), and the feature factor Zbl and the feature factor Zbr are 2 Calculated by dividing by.
  • the feature factor dZ is a forward / backward tilt (referred to as “front / back tilt”) in the YZ plane, and the value of the Y coordinate of the feature point (1) from the value of the Y coordinate of the feature point (2). Is divided by the value obtained by subtracting the value of the Z coordinate of the feature point (1) from the value of the Z coordinate of the feature point (2).
  • the characteristic factor StlShi is the sum of the amplitudes in the left diagonal direction in the YZ plane (referred to as “left front-rear amplitude”), the distance between the characteristic points (2) and (1) in the YZ plane, and the characteristics in the YZ plane. It is calculated by adding the distance between the point (2) and the feature point (3).
  • the distance between the feature point (2) and the feature point (1) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (1) from the Z coordinate value of the feature point (2), and the feature point ( It is calculated as the square root of the sum of the value obtained by subtracting the Y coordinate value of the feature point (1) and the square of the Y coordinate value of 2).
  • the distance between the feature point (2) and the feature point (3) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (3) from the Z coordinate value of the feature point (2), and the feature point ( It is calculated as the square root of the sum of the value obtained by subtracting the Y coordinate value of the feature point (3) and the square of the Y coordinate value of 2).
  • the feature factor StrShi is the sum of the amplitudes in the right diagonal direction in the YZ plane (referred to as “right front-rear amplitude”), the distance between the feature points (4) and (3) in the YZ plane, and the features in the YZ plane. It is calculated by adding the distance between the point (4) and the feature point (1).
  • the distance between the feature point (4) and the feature point (3) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (3) from the Z coordinate value of the feature point (4), and the feature point ( It is calculated as the square root of the sum of the square of the value obtained by subtracting the Y coordinate value of the feature point (3) from the Y coordinate value of 4).
  • the distance between the feature point (4) and the feature point (1) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (1) from the Z coordinate value of the feature point (4), and the feature point ( It is calculated as the square root of the sum of the square of the value obtained by subtracting the Y coordinate value of the feature point (1) from the Y coordinate value of 4).
  • the feature factor StShi is the sum of the amplitudes in the oblique direction in the YZ plane (referred to as “front-rear amplitude”), and is calculated by adding the feature factor StlShi and the feature factor StrShi and dividing by two.
  • FIG. 13 is a first diagram for explaining the correlation between the feature factor and the step length of the index indicating the walking posture in this embodiment.
  • FIG. 14 is a second diagram for illustrating the correlation between the feature factor and the stride among the indices indicating the walking posture in this embodiment.
  • the characteristic factor Hr of the locus pattern projected on the XZ plane described in FIG. 10 is the vertical axis (y), and the actually measured value of the stride, which is one of the indices indicating the walking posture, is obtained.
  • the characteristic factor StShi of the locus pattern projected on the YZ plane described in FIG. 12 is taken as the vertical axis (y), and the actually measured value of the stride, which is one of the indices indicating the walking posture, is obtained.
  • the stride which is an index indicating the walking posture, has a high correlation with the feature factor Hr and the feature factor StShi. Therefore, by performing multiple regression analysis, the feature factor Hr and the feature factor StShi as objective variables and the explanation are explained.
  • ⁇ , ⁇ , and ⁇ are partial regression coefficients obtained by multiple regression analysis.
  • FIG. 15 is a first diagram for explaining the correlation between the feature factor and the walking distance among the indices indicating the walking posture in this embodiment.
  • FIG. 16 is a second diagram for explaining the correlation between the feature factor and the step indicating the walking posture in this embodiment.
  • the step which is an index indicating the walking posture, is subjected to a multiple regression analysis with a multiple regression analysis of the characteristic factor Hr / W and the characteristic factor WuSu / WdSu as objective variables and the stride value as an explanatory variable.
  • Step width Width ⁇ ⁇ Hr / W + ⁇ ⁇ WuSu / WdSu + ⁇ can be used to calculate the step value. Note that ⁇ , ⁇ , and ⁇ are coefficients obtained by multiple regression analysis.
  • FIG. 17 is a block diagram showing an outline of the configuration of the activity meter 100 in this embodiment.
  • activity meter 100 includes a control unit 110, a memory 120, an operation unit 130, a display unit 140, an acceleration sensor 170, and a power source 190. Further, the activity meter 100 may include a sound report unit for outputting sound and an interface for communicating with an external computer.
  • control unit 110 the memory 120, the operation unit 130, the display unit 140, the acceleration sensor 170, and the power source 190 are incorporated in the main body unit 191 described with reference to FIG.
  • the operation unit 130 includes the display change / decision switch 131, the left operation / memory switch 132, and the right operation switch 133 described with reference to FIG. 1, and an operation signal indicating that these switches have been operated is sent to the control unit 110. Send.
  • the acceleration sensor 170 is a semiconductor type of MEMS (Micro Electro Mechanical Systems) technology, but is not limited to this, and may be of another type such as a mechanical type or an optical type. In the present embodiment, acceleration sensor 170 outputs a detection signal indicating the acceleration in each of the three axial directions to control unit 110. However, the acceleration sensor 170 is not limited to the three-axis type, and may be one-axis or two-axis type.
  • MEMS Micro Electro Mechanical Systems
  • the memory 120 includes non-volatile memory such as ROM (Read Only Memory) (for example, flash memory) and volatile memory such as RAM (Random Access Memory) (for example, SDRAM (synchronous Dynamic Random Access Memory)).
  • ROM Read Only Memory
  • RAM Random Access Memory
  • SDRAM synchronous Dynamic Random Access Memory
  • the memory 120 includes program data for controlling the activity meter 100, data used for controlling the activity meter 100, setting data for setting various functions of the activity meter 100, and the number of steps and activities. Measurement result data such as quantity is stored every predetermined time (for example, every day). The memory 120 is used as a work memory when the program is executed.
  • the control unit 110 includes a CPU (Central Processing Unit), and according to an operation signal from the operation unit 130 according to a program for controlling the activity meter 100 stored in the memory 120, the acceleration sensor 170 and the atmospheric pressure sensor 180.
  • the memory 120 and the display unit 140 are controlled on the basis of the detection signal from.
  • the display unit 140 includes the display 141 described with reference to FIG. 1 and controls the display 141 to display predetermined information according to a control signal from the control unit 110.
  • the power source 190 includes a replaceable battery, and supplies power from the battery to each unit that requires power to operate, such as the control unit 110 of the activity meter 100.
  • FIG. 18 is a functional block diagram showing an outline of the function of the activity meter 100 in this embodiment.
  • the control unit 110 of the activity meter 100 includes an acceleration reading control unit 111, a feature point position specifying unit 112, a feature factor calculation unit 113, an index calculation unit 114, and a walking posture determination unit 115. And a display control unit 116.
  • the storage unit 120 of the activity meter 100 includes an acceleration data storage unit 121, a feature point position storage unit 122, a feature factor storage unit 123, a correlation storage unit 124, and an index storage unit 125.
  • these units included in the control unit 110 are configured in the control unit 110 by executing software for executing the processing of FIG. 19 described later by the control unit 110. I will do it.
  • the present invention is not limited to this, and each of these units included in the control unit 110 may be configured inside the control unit 110 as a hardware circuit.
  • Each of these units included in the storage unit 120 is temporarily configured in the storage unit 120 when the control unit 110 executes software for executing the processing of FIG. .
  • the present invention is not limited to this, and each of these units included in the storage unit 120 may be configured as a dedicated storage device.
  • each of these units included in the storage unit 120 may be temporarily configured in a built-in memory of the control unit 110 such as a register instead of being configured in the storage unit 120.
  • the acceleration reading control unit 111 detects accelerations Ax (t), Ay (t), and Az (t) in three axes from the acceleration sensor 170.
  • acceleration data Ax (t), Ay (t), Az (t) in the Z-axis direction are directly used as the X-axis and Y-axis.
  • acceleration data Ax (t), Ay (t), Az (t) in the Z-axis direction are directly used as the X-axis and Y-axis.
  • the X-axis, Y-axis, and Z-axis directions are converted by coordinate conversion of the detection values obtained by the acceleration sensor.
  • the respective acceleration data Ax (t), Ay (t), Az (t) are calculated.
  • the acceleration reading control unit 111 stores the acceleration data Ax (t), Ay (t), and Az (t) calculated for each sampling period in the acceleration data storage unit 121 of the storage unit 120.
  • the feature point position specifying unit 112 is based on the acceleration data Ax (t), Ay (t), Az (t) stored in the acceleration data storage unit 121, as described with reference to FIG.
  • the relative position X (t) with respect to the average position in a short time (here, ⁇ 1 step time ( ⁇ T seconds)) of the activity meter 100 in the X-axis, Y-axis, and Z-axis directions , Y (t), Z (t) are calculated.
  • the feature point position specifying unit 112 uses the method described with reference to FIGS. 7 to 9 based on the calculated positions X (t), Y (t), and Z (t) to determine the feature points. Specify the coordinate value of the position.
  • the feature point position specifying unit 112 is based on the acceleration detected by the acceleration sensor 170, and the orthogonal three-axis directions of the Y-axis direction (vertical direction), the Z-axis direction (traveling direction), and the X-axis direction (left-right direction).
  • the position of the feature point of the trajectory projected by removing the moving component in the Z-axis direction on the XZ plane, the XY plane, and the YZ plane, which are planes perpendicular to each other, is specified.
  • the feature point position specifying unit 112 causes the feature point position storage unit 122 to store the calculated position of the feature point.
  • the feature factor calculation unit 113 calculates the value of the feature factor according to the calculation formulas described with reference to FIGS. 10 to 12 based on the position of the feature point stored in the feature point position storage unit 122. Then, the feature factor calculation unit 113 stores the calculated feature factor value in the feature factor storage unit 123.
  • the correlation storage unit 124 stores in advance the multiple regression equations described above with reference to FIGS.
  • the index calculation unit 114 based on the feature factor value stored in the feature factor storage unit 123, in accordance with the multiple regression equation stored in the correlation storage unit 124, indicates an index indicating the walking posture (for example, stride, step, Rotation of hips, leg height, back muscle stretch, center of gravity balance, etc.). Then, the index calculation unit 114 stores the calculated index value in the index storage unit 125.
  • the walking posture for example, stride, step, Rotation of hips, leg height, back muscle stretch, center of gravity balance, etc.
  • the walking posture determination unit 115 determines the walking posture based on the index value stored in the index storage unit 125.
  • FIG. 19 is a first diagram showing an example of the determination of the walking posture in this embodiment. Referring to FIG. 19, as shown in FIG. 19 (A), when the stride is wider than the predetermined threshold, as shown in FIG. 19 (B), the walking posture is smaller than the stride whose width is smaller than the predetermined threshold. May be judged as good.
  • the walking posture is better when the step is narrower than the predetermined threshold than when the step is wider than the predetermined threshold as shown in FIG. It is possible to judge.
  • FIG. 20 is a second diagram showing an example of the determination of the walking posture in this embodiment.
  • the step length is L
  • the step interval is W
  • the hip rotation is D
  • the foot-lifting height is H
  • the back stretch is B.
  • Threshold values for classifying the stride length L are a, b, c (a ⁇ b ⁇ c).
  • thresholds for classifying the step W are d, e, f (d ⁇ e ⁇ f).
  • the thresholds for classifying the hip rotation D are set as h, i, j (h ⁇ i ⁇ j).
  • Threshold values for classifying the foot height H are k, l, and m (k ⁇ l ⁇ m).
  • the thresholds for classifying the back stretch B are n, o, and p (n ⁇ o ⁇ p).
  • the determination of the walking posture can be performed in combination with various indexes to make a detailed determination according to the user.
  • the display control unit 116 controls the display unit 140 to display the determination result of the walking posture determined by the walking posture determination unit 115.
  • the activity meter 100 may be connected to an external device such as a personal computer, and the determination result may be displayed on the display unit of the external device.
  • FIG. 21 is a diagram showing an example of the display of the walking posture determination result in this embodiment. Referring to FIG. 21, it is displayed at the top of the right column that the goal is “I want to lose weight”, “I want to maintain physical strength”, and “I want to walk younger”. Yes. This target is set in advance by the user selecting from several target candidate options.
  • an image of the target walking posture viewed from the front and from the side is displayed.
  • an image of the user's walking posture generated based on the calculated index indicating the walking posture as viewed from the front and an image as viewed from the side are displayed.
  • a mark an oval box in the figure is displayed on a body part related to an index having a low evaluation among the indexes indicating the walking posture.
  • a radar chart for stride, step and hip rotation, and a chart showing where the balance of the center of gravity is located on the left and right of the index indicating the walking posture are displayed at the top.
  • the target walking posture index value is indicated by a rhombus plot
  • the user walking posture index value is indicated by a square plot.
  • the target value for the stride, step, and hip rotation is the third stage of each of the four stages, whereas the user value is the second stage and the third stage, respectively.
  • the first stage is shown.
  • the center of gravity balance chart shows that the balance of the user is to the right while the balance of the target is the center.
  • the waist position is marked in the image of the walking posture of the user in the right column.
  • the foot color may be red.
  • the advice to the user is displayed under the chart in the left column.
  • advice such as “Wide the stride, rotate the hips and walk” is displayed.
  • Such advice is stored in advance in the storage unit 120 of the activity meter 100 in accordance with the degree of deviation from the target, and the walking posture determination unit 115 determines the The advice corresponding to the degree of deviation is selected, and the selected advice is displayed on the display unit 140 by the display control unit 116.
  • training content for approaching the target walking posture may be displayed instead of or together with the advice.
  • FIG. 22 is a diagram showing an example of the display of the user's walking posture image in this embodiment.
  • FIG. 22 (A) is an image showing a standard walking posture.
  • FIG. 22B is an image showing a walking posture when the stride is large.
  • FIG. 22C is an image showing a walking posture when the center of gravity balance is on the right side.
  • FIG. 23 is a flowchart showing the flow of the walking posture determination process executed by the control unit 110 of the activity meter 100 in this embodiment.
  • control unit 110 reads the detected value of the acceleration sensor from acceleration sensor 170, and as described in acceleration reading control unit 111 in FIG. 18, acceleration data Ax (t), Ay (T) and Az (t) are stored in the storage unit 120 for each sampling period.
  • step S102 the control unit 110 determines whether or not one step of walking has been detected. Here, it is determined that one step has been detected by detecting the feature point (1) (feature point (5)) described in FIG. If it is determined that one step has not been detected (NO in step S102), control unit 110 advances the process to be executed to step S111.
  • step S103 the control unit 110 stores acceleration data Ax (t for one step stored in the storage unit 120 in step S101. ), Ay (t), Az (t) are read, and the coordinate value of the position of the feature point is calculated as described in the feature point position specifying unit 112 in FIG.
  • step S104 the control unit 110 calculates the value of the feature factor based on the coordinate value of the position of the feature point calculated in step S103, as described in the feature factor calculation unit 113 in FIG. .
  • step S105 the control unit 110 follows the correlation between the feature factor and the walking posture index as described in the index calculation unit 114 of FIG. 18 based on the value of the feature factor calculated in step S104.
  • the index value is calculated and stored in the storage unit 120. Then, the control part 110 advances the process to perform to the process of step S11.
  • step S111 the control unit 110 determines whether or not an instruction to display the determination result of the walking posture is received by the operation unit 130 being operated by the user. If it is determined that the result display instruction has not been received (NO in step S111), control unit 110 returns the process to be executed to the process in step S101.
  • step S112 the control unit 110 reads the index indicating the walking posture stored in the storage unit 120 in step S105, Based on the index, the walking posture is determined as described in the walking posture determination unit 115 of FIG.
  • step S113 the control unit 110 controls the display unit 140 to display the determination result of the walking posture determined in step S112, as described in the display control unit 116 of FIG. Then, the control part 110 returns the process to perform to the process of step S101.
  • the activity meter 100 includes the main body 191, the acceleration sensor 170, and the control unit 110, and the walking posture of the user who wears the main body 191 on the waist. It is an apparatus for determining.
  • the three-dimensional trajectory from which the moving component in the advancing direction (Z-axis direction) during walking of the waist to which the main body 191 is attached has the pattern described with reference to FIGS.
  • the pattern includes a plurality of feature points that define the features of the pattern.
  • the control unit 110 is an XZ plane that is a plane perpendicular to the vertical direction (Y-axis direction), the traveling direction (Z-axis direction), and the left-right direction (X-axis direction).
  • the feature point position specifying unit 112 that specifies the position of the feature point of the locus projected by removing the moving component in the traveling direction (Z-axis direction) on the XY plane and the YZ plane, and the feature point position specifying unit 112
  • the feature factor calculation unit 113 that calculates the value of the feature factor of the trajectory based on the determined position, and the feature factor calculation unit 113 according to the correlation obtained in advance between the value of the feature factor and the value of the index indicating the walking posture.
  • An index calculation unit 114 that calculates an index value based on the calculated feature factor value, and a walking posture determination that determines a walking posture based on the index value calculated by the index calculation unit 114 And a part 115.
  • the index indicating the walking posture is accurately calculated based on the accurate correlation, and the index indicating the various walking postures is calculated, the detailed walking posture can be evaluated more accurately.
  • the activity meter 100 further includes a display unit 140.
  • the control unit 110 includes a display control unit 116 that causes the display unit 140 to display the walking posture determined by the walking posture determination unit 115 and the target walking posture in a comparable manner. For this reason, the user's walking posture can be easily displayed.
  • control unit 110 includes a display control unit 116 that displays on the display unit 140 advice for improving the walking posture determined by the walking posture determination unit 115. For this reason, the user's walking posture can be easily displayed.
  • the correlation is a relational expression obtained by multiple regression analysis, which is a relational expression between a characteristic factor value as a target variable and an index value as an explanatory variable. It is shown by a regression equation.
  • the feature points are the feature point (1) when the right foot touches down, the feature point (2) when the locus reaches the highest position while standing on the right foot, and the left foot This includes a feature point (3) when the robot touches down and a feature point (4) when the locus reaches the highest position while standing on the left foot.
  • the feature factor is a feature factor Hr that is the distance in the vertical direction (Y-axis direction) between the feature point (1) and the feature point (2) in the locus projected on the XY plane perpendicular to the traveling direction (Z-axis direction), and , Calculated from the distance between the feature point (1) and the feature point (2) and the distance between the feature point (3) and the feature point (4) in the locus projected on the YZ plane perpendicular to the left-right direction (X-axis direction).
  • the characteristic factor StShi is included.
  • the index includes stride.
  • the feature points are the feature point (1) when the right foot touches down, the feature point (2) when the trajectory reaches the highest position while standing on the right foot, and the rightmost point of the trajectory.
  • the feature point (6), the leftmost feature point (7) of the locus, the foremost feature point (10) on the right side of the locus, the foremost feature point (12) on the left side of the locus, and the right side of the locus And the rearmost feature point (9) and the rearmost feature point (11) on the left side of the locus.
  • the feature factor is a distance Hr in the vertical direction (Y-axis direction) between the feature point (1) and the feature point (2) in the locus projected on the XY plane perpendicular to the traveling direction (Z-axis direction).
  • a feature factor Hr / W which is a quotient divided by a distance W in the left-right direction (X-axis direction) from the feature point (7), and a feature in the locus projected on the XZ plane perpendicular to the vertical direction (Y-axis direction)
  • the quotient obtained by dividing the distance WuSu between the point (10) and the feature point (12) in the left-right direction (X-axis direction) by the distance WdSu between the feature point (9) and the feature point (11) in the left-right direction (X-axis direction).
  • the characteristic factor WuSu / WdSu is included.
  • the index includes the step interval.
  • the walking posture is determined based on the relationship between the index value and the threshold value.
  • the present invention is not limited to this, and the walking posture may be determined on the basis of the similarity between the combination of the index whose relationship with the walking posture is obtained in advance and the calculated combination of the index.
  • the threshold value of the index indicating the walking posture may be determined based on actually measured data when a person with a good walking posture actually walks.
  • the target walking posture and the user's walking posture are displayed separately as described in FIG.
  • the present invention is not limited to this, and the target walking posture and the user's walking posture may be displayed in an overlapping manner.
  • the average speed component is an average speed component for the time of ⁇ 1 step.
  • the present invention is not limited to this, and it may be an average speed component for a time of ⁇ n steps (n is a predetermined number) or a time of ⁇ n steps (n steps before the calculation target time). It may be an average velocity component, may be an average velocity component of ⁇ s seconds (s is a predetermined number), or may be an average velocity component of -s seconds (before calculation target, s seconds).
  • the invention of the device for the activity meter 100 has been described.
  • the present invention is not limited to this, and can be understood as an invention of a control method for controlling the activity meter 100.

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Abstract

The gait posture assessment device of the present invention is provided with a main unit, an acceleration sensor for detecting acceleration of the main unit, and a control unit, and is a device for assessing the gait posture of a user wearing the main unit upon a predetermined region. A three-dimensional trajectory of the predetermined region excluding the component of movement in the direction of travel when walking has a pattern, wherein the pattern contains a plurality of feature points which define the features thereof. A controller, on the basis of the acceleration detected by an acceleration sensor, identifies positions of feature points of the trajectory which has been projected after removing the component of movement of the direction of travel from surfaces perpendicular to each of three axes orthogonal to one another, which are the vertical direction, the direction of travel, and the left and right directions (S103), calculates the values of characteristic factors of the trajectory on the basis of the identified positions (S104), and according to the correlation relation which has been obtained in advance between the values of the characteristic factors and the values of the indices indicating the gait posture, calculates values of the indices on the basis of the values of the calculated characteristic factors (S105), and assesses the gait posture on the basis of the calculated values of the indices (S112). Consequently, it is possible to perform a more precise and detailed evaluation of gait posture.

Description

歩行姿勢判定装置Walking posture determination device
 この発明は、歩行姿勢判定装置に関し、特に、当該装置を所定部位に装着するユーザの歩行姿勢を判定するのに適した歩行姿勢判定装置に関する。 The present invention relates to a walking posture determination device, and more particularly to a walking posture determination device suitable for determining a walking posture of a user who wears the device at a predetermined site.
 従来、3軸の加速度データに基づいて、歩行の進行方向に垂直な面に投影した腰部の軌跡、鉛直方向に垂直な面に投影した腰部の軌跡、および、左右方向に垂直な面に投影した腰部の軌跡を用いて、歩行時の姿勢を判断するものがあった(たとえば、特許文献1(特開2009-106374号公報)の請求項1および図6参照)。 Conventionally, based on triaxial acceleration data, the locus of the waist projected on the surface perpendicular to the direction of travel of the walking, the locus of the waist projected on the surface perpendicular to the vertical direction, and the surface projected perpendicular to the left-right direction There has been a technique for judging the posture during walking using the locus of the waist (see, for example, claim 1 of Patent Document 1 (Japanese Patent Laid-Open No. 2009-106374) and FIG. 6).
 また、同様の軌跡を用いて、歩行介助において歩行運動の改善効果を客観的に評価するものがあった(たとえば、特許文献2(特開2006-102156号公報)の図11参照)。 In addition, there has been an object that objectively evaluates the improvement effect of walking motion in walking assistance using a similar locus (see, for example, FIG. 11 of Patent Document 2 (Japanese Patent Laid-Open No. 2006-102156)).
特開2009-106374号公報JP 2009-106374 A 特開2006-102156号公報JP 2006-102156 A
 しかし、従来の技術では、変化の幅のみを用いているため、より詳細な歩行姿勢を評価できないといった問題があった。また、姿勢がどのくらい目標とずれているかが分かり難いといった問題があった。 However, since the conventional technique uses only the width of change, there is a problem that a more detailed walking posture cannot be evaluated. In addition, there is a problem that it is difficult to understand how much the posture deviates from the target.
 この発明は、上述の問題を解決するためになされたものであり、その目的の1つは、より精度よく詳細な歩行姿勢を評価することが可能な歩行姿勢判定装置を提供することである。 The present invention has been made to solve the above-described problems, and one of its purposes is to provide a walking posture determination apparatus capable of evaluating a detailed walking posture with higher accuracy.
 この発明の他の目的は、歩行姿勢の状況を判り易く表示することが可能な歩行姿勢判定装置を提供することである。 Another object of the present invention is to provide a walking posture determination device capable of displaying the walking posture in an easily understandable manner.
 上述の目的を達成するために、この発明のある局面によれば、歩行姿勢判定装置は、本体部と、本体部の加速度を検出するための加速度センサと、制御部とを備え、本体部を所定部位に装着するユーザの歩行姿勢を判定するための装置である。本体部が装着される所定部位の歩行時の進行方向の移動成分を除去した3次元の軌跡は、パターンを有する。当該パターンは、当該パターンの特徴を規定する特徴点を複数含む。 In order to achieve the above-described object, according to one aspect of the present invention, a walking posture determination device includes a main body, an acceleration sensor for detecting acceleration of the main body, and a controller. It is an apparatus for determining the walking posture of a user wearing a predetermined part. A three-dimensional trajectory from which a moving component in the traveling direction during walking of a predetermined part to which the main body is attached has a pattern. The pattern includes a plurality of feature points that define the features of the pattern.
 制御部は、加速度センサによって検出された加速度に基づいて、鉛直方向、進行方向および左右方向の直交3軸方向のそれぞれに垂直な面に進行方向の移動成分を除去して投影した軌跡の特徴点の位置を特定する特定部と、特定部によって特定された位置に基づいて、軌跡の特徴因子の値を算出する第1の算出部と、特徴因子の値と歩行姿勢を示す指標の値との予め求められた相関関係に従って、第1の算出部によって算出された特徴因子の値に基づいて、指標の値を算出する第2の算出部と、第2の算出部によって算出された指標の値に基づいて、歩行姿勢を判定する判定部とを含む。 Based on the acceleration detected by the acceleration sensor, the control unit removes the moving component in the traveling direction on the plane perpendicular to each of the three orthogonal directions of the vertical direction, the traveling direction, and the left-right direction. A specifying unit that specifies the position of the first position, a first calculation unit that calculates a value of the characteristic factor of the trajectory based on the position specified by the specifying unit, a value of the characteristic factor and an index value indicating the walking posture A second calculation unit that calculates an index value based on a characteristic factor value calculated by the first calculation unit according to a correlation obtained in advance, and an index value calculated by the second calculation unit And a determination unit for determining a walking posture based on
 好ましくは、歩行姿勢判定装置は、さらに、表示部を備える。制御部は、さらに、判定部によって判定された歩行姿勢と目標の歩行姿勢とを比較可能に表示部に表示させる表示制御部を含む。 Preferably, the walking posture determination device further includes a display unit. The control unit further includes a display control unit that causes the display unit to display the walking posture determined by the determination unit and the target walking posture in a comparable manner.
 好ましくは、歩行姿勢判定装置は、さらに、表示部を備える。制御部は、さらに、判定部によって判定された歩行姿勢を改善するためのアドバイスを表示部に表示させる表示制御部を含む。 Preferably, the walking posture determination device further includes a display unit. The control unit further includes a display control unit that causes the display unit to display advice for improving the walking posture determined by the determination unit.
 好ましくは、相関関係は、重回帰分析によって得られる、目的変数としての特徴因子の値と説明変数としての指標の値との関係式である重回帰式で示される。 Preferably, the correlation is represented by a multiple regression equation that is a relational expression between a characteristic factor value as an objective variable and an index value as an explanatory variable, obtained by multiple regression analysis.
 さらに好ましくは、特徴点は、第1の足が接地したときの第1の特徴点、および、第1の足で立脚している間で軌跡が最も高い位置に達したときの第2の特徴点、ならびに、第2の足が接地したときの第3の特徴点、および、第2の足で立脚している間で軌跡が最も高い位置に達したときの第4の特徴点を含む。 More preferably, the feature points are a first feature point when the first foot is grounded and a second feature when the locus reaches the highest position while standing on the first foot. And a third feature point when the second foot comes in contact with the ground, and a fourth feature point when the locus reaches the highest position while standing on the second foot.
 特徴因子は、進行方向に垂直な面に投影した軌跡における第1の特徴点と第2の特徴点との鉛直方向の距離である第1の特徴因子、および、左右方向に垂直な面に投影した軌跡における第1の特徴点と第2の特徴点との距離および第3の特徴点と第4の特徴点との距離から算出される第2の特徴因子を含む。 The feature factor is a first feature factor that is the distance in the vertical direction between the first feature point and the second feature point in the locus projected on the plane perpendicular to the traveling direction, and the feature factor is projected onto the plane perpendicular to the left-right direction. The second feature factor calculated from the distance between the first feature point and the second feature point in the locus and the distance between the third feature point and the fourth feature point is included.
 指標は、歩幅を含む。重回帰式は、重回帰分析によって得られた第1の偏回帰係数および第1の特徴因子の積と、重回帰分析によって得られた第2の偏回帰係数および第2の特徴因子の積と、第3の偏回帰係数との和を算出する式である。 * The index includes stride. The multiple regression equation includes the product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, and the product of the second partial regression coefficient and the second feature factor obtained by the multiple regression analysis. , Is a formula for calculating the sum with the third partial regression coefficient.
 さらに好ましくは、特徴点は、第1の足が接地したときの第1の特徴点、第1の足で立脚している間で軌跡が最も高い位置に達したときの第2の特徴点、軌跡の最も右側の第3の特徴点、および、軌跡の最も左側の第4の特徴点、ならびに、軌跡の右側で最も前側の第5の特徴点、軌跡の左側で最も前側の第6の特徴点、軌跡の右側で最も後ろ側の第7の特徴点、および、軌跡の左側で最も後ろ側の第8の特徴点を含む。 More preferably, the feature point is a first feature point when the first foot is grounded, a second feature point when the locus reaches the highest position while standing on the first foot, The third feature point on the rightmost side of the locus, the fourth feature point on the leftmost side of the locus, the fifth feature point on the most front side on the right side of the locus, and the sixth feature on the leftmost side of the locus. A point, the seventh feature point on the backmost side on the right side of the trajectory, and the eighth feature point on the backmost side on the left side of the trajectory.
 特徴因子は、進行方向に垂直な面に投影した軌跡における第1の特徴点と第2の特徴点との鉛直方向の距離を第3の特徴点と第4の特徴点との左右方向の距離で割った商である第1の特徴因子、および、鉛直方向に垂直な面に投影した軌跡における第5の特徴点と第6の特徴点との左右方向の距離を第7の特徴点と第8の特徴点との左右方向の距離で割った商である第2の特徴因子を含む。 The feature factor is the distance in the horizontal direction between the third feature point and the fourth feature point in the vertical direction between the first feature point and the second feature point in the trajectory projected on the plane perpendicular to the traveling direction. And the distance in the left-right direction between the fifth feature point and the sixth feature point in the trajectory projected on the surface perpendicular to the vertical direction, and the seventh feature point and the A second feature factor that is a quotient divided by the distance in the left-right direction from the eight feature points.
 指標は、歩隔を含む。重回帰式は、重回帰分析によって得られた第1の偏回帰係数および第1の特徴因子の積と、重回帰分析によって得られた第2の偏回帰係数および第2の特徴因子の積と、第3の偏回帰係数との和を算出する式である。 * The index includes the step interval. The multiple regression equation includes the product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, and the product of the second partial regression coefficient and the second feature factor obtained by the multiple regression analysis. , Is a formula for calculating the sum with the third partial regression coefficient.
この発明に従えば、本体部が装着される所定部位の歩行時の進行方向の移動成分を除去した3次元の軌跡は、パターンを有し、当該パターンは、当該パターンの特徴を規定する特徴点を複数含み、加速度センサによって検出された加速度に基づいて、鉛直方向、進行方向および左右方向の直交3軸方向のそれぞれに垂直な面に進行方向の移動成分が除去されて投影された軌跡の特徴点の位置が特定され、特定された位置に基づいて、軌跡の特徴因子の値が算出され、算出された特徴因子の値に基づいて、特徴因子の値と歩行姿勢を示す指標の値との予め求められた相関関係に従って、指標の値が算出され、算出された指標の値に基づいて、歩行姿勢が判定される。 According to the present invention, the three-dimensional trajectory from which the moving component in the advancing direction during walking of the predetermined part to which the main body is attached has a pattern, and the pattern is a feature point that defines the feature of the pattern. Of the trajectory projected by removing the moving component in the traveling direction on a plane perpendicular to each of the three orthogonal directions perpendicular to the vertical direction, the traveling direction, and the left-right direction based on the acceleration detected by the acceleration sensor The position of the point is specified, the value of the trait feature factor is calculated based on the specified position, and the value of the feature factor and the index value indicating the walking posture are calculated based on the calculated feature factor value. The index value is calculated according to the correlation obtained in advance, and the walking posture is determined based on the calculated index value.
 その結果、より精度よく詳細な歩行姿勢を評価することが可能な歩行姿勢判定装置を提供することができる。 As a result, it is possible to provide a walking posture determination device capable of evaluating a detailed walking posture with higher accuracy.
この発明の実施の形態における活動量計の外観図である。It is an external view of the active mass meter in embodiment of this invention. この実施の形態における活動量計の使用状態を示す図である。It is a figure which shows the use condition of the active mass meter in this embodiment. ユーザの歩行時の腰の軌跡を歩行の進行方向から見た第1の例を示す図である。It is a figure which shows the 1st example which looked at the locus | trajectory of the waist at the time of a user's walk from the advancing direction of walk. ユーザの歩行時の腰の軌跡を歩行の進行方向から見た第2の例を示す図である。It is a figure which shows the 2nd example which looked at the locus | trajectory of the waist at the time of a user's walk from the advancing direction of walk. ユーザの歩行時の腰の軌跡を歩行の進行方向から見た複数の例を示す図である。It is a figure which shows the several example which looked at the locus | trajectory of the waist at the time of a user's walk from the advancing direction of walk. この実施の形態において加速度データから算出したユーザの歩行時の腰の軌跡と実測したユーザの歩行時の腰の軌跡との相関を示す図である。It is a figure which shows the correlation with the locus | trajectory of the waist at the time of the user's walk calculated from the acceleration data in this embodiment, and the waist locus at the time of the user's walk measured. この実施の形態においてXY平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。It is a figure for demonstrating the feature point contained in the pattern of the locus | trajectory projected on XY plane in this embodiment. この実施の形態においてXZ平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。It is a figure for demonstrating the feature point contained in the pattern of the locus | trajectory projected on the XZ plane in this embodiment. この実施の形態においてYZ平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。It is a figure for demonstrating the feature point contained in the pattern of the locus | trajectory projected on the YZ plane in this embodiment. この実施の形態においてXY平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。It is a figure for demonstrating the feature factor calculated based on the position of the feature point contained in the pattern of the locus | trajectory projected on XY plane in this embodiment. この実施の形態においてXZ平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。It is a figure for demonstrating the feature factor calculated based on the position of the feature point contained in the pattern of the locus | trajectory projected on the XZ plane in this embodiment. この実施の形態においてYZ平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。It is a figure for demonstrating the feature factor calculated based on the position of the feature point contained in the pattern of the locus | trajectory projected on the YZ plane in this embodiment. この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩幅との相関関係を説明するための第1の図である。It is a 1st figure for demonstrating the correlation with a step among the parameter | indexes which show the characteristic factor and walking posture in this embodiment. この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩幅との相関関係を説明するための第2の図である。It is a 2nd figure for demonstrating the correlation with a step among the parameter | indexes which show the characteristic factor and walking posture in this embodiment. この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩隔との相関関係を説明するための第1の図である。It is a 1st figure for demonstrating the correlation with a step among the parameters | indexes which show the characteristic factor and walking posture in this embodiment. この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩隔との相関関係を説明するための第2の図である。It is a 2nd figure for demonstrating the correlation with a step among the parameters | indexes which show the characteristic factor and walking posture in this embodiment. この実施の形態における活動量計の構成の概略を示すブロック図である。It is a block diagram which shows the outline of a structure of the active mass meter in this embodiment. この実施の形態における活動量計の機能の概略を示す機能ブロック図である。It is a functional block diagram which shows the outline of the function of the active mass meter in this embodiment. この実施の形態における歩行姿勢の判定の一例を示す第1の図である。It is a 1st figure which shows an example of the determination of the walking posture in this embodiment. この実施の形態における歩行姿勢の判定の一例を示す第2の図である。It is a 2nd figure which shows an example of the determination of the walking posture in this embodiment. この実施の形態における歩行姿勢の判定結果の表示の一例を示す図である。It is a figure which shows an example of the display of the determination result of the walking posture in this embodiment. この実施の形態におけるユーザの歩行姿勢の画像の表示の一例を示す図である。It is a figure which shows an example of a display of the image of the user's walking posture in this embodiment. この実施の形態における活動量計の制御部によって実行される歩行姿勢判定処理の流れを示すフローチャートである。It is a flowchart which shows the flow of the walking posture determination process performed by the control part of the active mass meter in this embodiment.
 以下、この発明の実施の形態について、図面を参照しながら詳細に説明する。なお、図中の同一または相当部分については、同一符号を付してその説明は繰返さない。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that the same or corresponding parts in the drawings are denoted by the same reference numerals and description thereof will not be repeated.
 本実施の形態においては、歩行姿勢判定装置が、歩数測定だけでなく、運動や生活活動(たとえば、掃除機をかける、軽い荷物運び、炊事など)における活動量(運動量ともいう)も測定することが可能な活動量計であることとして実施の形態を説明する。しかし、これに限定されず、歩行姿勢判定装置は、歩数測定が可能な歩数計であってもよい。 In the present embodiment, the walking posture determination device measures not only the number of steps, but also the amount of activity (also referred to as the amount of exercise) in exercise and daily activities (for example, vacuuming, carrying light luggage, cooking, etc.). The embodiment will be described as an activity meter capable of However, the present invention is not limited to this, and the walking posture determination device may be a pedometer capable of measuring the number of steps.
 図1は、この発明の実施の形態における活動量計100の外観図である。図1を参照して、活動量計100は、本体部191と、クリップ部192とから主に構成される。クリップ部192は、活動量計100をユーザの着衣などに固定するために用いられる。 FIG. 1 is an external view of an activity meter 100 according to the embodiment of the present invention. With reference to FIG. 1, the activity meter 100 is mainly composed of a main body portion 191 and a clip portion 192. The clip unit 192 is used to fix the activity meter 100 to a user's clothes or the like.
 本体部191には、後述する操作部130の一部を構成する表示切換/決定スイッチ131、左操作/メモリスイッチ132、および、右操作スイッチ133、ならびに、後述する表示部140の一部を構成するディスプレイ141が設けられる。 The main body 191 includes a display change / decision switch 131, a left operation / memory switch 132, a right operation switch 133, and a part of a display unit 140, which will be described later. A display 141 is provided.
 本実施の形態においては、ディスプレイ141は、液晶ディスプレイ(LCD:LiquidCrystal Display)で構成されることとするが、これに限定されず、EL(ElectroLuminescence)ディスプレイなど他の種類のディスプレイであってもよい。 In the present embodiment, display 141 is configured by a liquid crystal display (LCD), but is not limited thereto, and may be another type of display such as an EL (ElectroLuminescence) display. .
 図2は、この実施の形態における活動量計100の使用状態を示す図である。図2を参照して、活動量計100は、たとえば、ユーザの腰部のベルトに、クリップ部192を用いて装着される。この実施の形態においては、活動量計100は、ユーザの腰の近辺に固定して装着されることが望ましい。 FIG. 2 is a diagram showing a usage state of the activity meter 100 in this embodiment. Referring to FIG. 2, activity meter 100 is attached to a belt on a user's waist using clip portion 192, for example. In this embodiment, it is desirable that the activity meter 100 is fixedly mounted near the user's waist.
 なお、本実施の形態において、歩行時のユーザの進行方向をZ軸(進む向きを正方向)とし、歩行時のユーザの左右方向をX軸(右向きを正方向)とし、鉛直方向をY軸(鉛直上向きを正方向)とする座標系を用いることとする。 In the present embodiment, the user's traveling direction during walking is the Z axis (the forward direction is the positive direction), the user's left and right direction during walking is the X axis (the right direction is the positive direction), and the vertical direction is the Y axis. A coordinate system with (vertically upward as a positive direction) is used.
 図3は、ユーザの歩行時の腰の軌跡を歩行の進行方向から見た第1の例を示す図である。図4は、ユーザの歩行時の腰の軌跡を歩行の進行方向から見た第2の例を示す図である。図3(A)および図4(A)は、歩行時の腰の軌跡をユーザの画像と重ねた図である。図3(B)および図4(B)は、ユーザの歩行時の腰の軌跡をグラフで表わした図である。 FIG. 3 is a diagram showing a first example in which the hip locus during walking of the user is viewed from the direction of walking. FIG. 4 is a diagram illustrating a second example in which the hip locus during walking of the user is viewed from the walking direction. FIG. 3A and FIG. 4A are diagrams in which the hip locus during walking is superimposed on the user's image. FIG. 3B and FIG. 4B are graphs showing the locus of the waist when the user walks.
 図3および図4を参照して、この軌跡は、歩行時において、Z軸に垂直な面であるXY平面に投影した軌跡である。歩行においては、通常、右足が地面から離されてから、右足が最も高い位置に到達した後に、右足が地面に接触し、次に、左足が地面から離されてから、左足が最も高い位置に到達した後に、左足が地面に接触するといった過程で足が動かされる。 Referring to FIG. 3 and FIG. 4, this locus is a locus projected on the XY plane, which is a plane perpendicular to the Z-axis, during walking. In walking, the right foot usually touches the ground after reaching the highest position after the right foot is released from the ground, and then the left foot is moved to the highest position after the left foot is released from the ground. After reaching, the foot is moved in the process of the left foot touching the ground.
 このような歩行の過程において、ユーザの腰の軌跡は、まず、右下から左上に向かい、左上の最も高い位置に到達した後に、左下に向かい、左下の最も低い位置に到達した後に、右上に向かい、右上の最も高い位置に到達した後に、右下に向かい、右下の最も低い位置に到達するといった特定のパターンとなる。 In such a walking process, the locus of the user's waist first moves from the lower right to the upper left, reaches the highest position on the upper left, then moves to the lower left, reaches the lowest position on the lower left, and then moves to the upper right. It is a specific pattern in which, after reaching the highest position on the upper right, heading to the lower right and reaching the lowest position on the lower right.
 図5は、ユーザの歩行時の腰の軌跡を歩行の進行方向から見た複数の例を示す図である。図5を参照して、図5(A)は、図3(B)で示した図と同様の図である。図5(A)は、普通の歩行姿勢のときのユーザの歩行時の腰の軌跡を示す。図5(B)は、図4(B)で示した図と同様の図である。図5(B)は、図5(A)の場合よりも、歩隔が広い、いわゆる、がにまたの場合のユーザの歩行時の腰の軌跡を示す。 FIG. 5 is a diagram showing a plurality of examples in which the hip locus during walking of the user is viewed from the direction of walking. Referring to FIG. 5, FIG. 5 (A) is a diagram similar to the diagram shown in FIG. 3 (B). FIG. 5A shows the locus of the waist when the user walks in the normal walking posture. FIG. 5B is a diagram similar to the diagram illustrated in FIG. FIG. 5B shows the locus of the waist when the user walks in a case where the step is wider than in the case of FIG.
 図5(C)は、図5(A)の場合よりも、歩隔が狭い場合のユーザの歩行時の腰の軌跡を示す。図5(D)は、図5(A)の場合よりも、すり足で歩いた場合のユーザの歩行時の腰の軌跡を示す。図5(E)は、図5(A)の場合よりも、猫背で歩いた場合のユーザの歩行時の腰の軌跡を示す。図5(F)は、図5(A)の場合よりも、大またで歩いた場合のユーザの腰の軌跡を示す。 FIG. 5C shows the locus of the waist when the user walks when the step is narrower than in the case of FIG. FIG. 5 (D) shows the locus of the waist when the user walks when walking with a skate rather than the case of FIG. 5 (A). FIG. 5 (E) shows the locus of the waist when the user walks when walking with the back of his back, rather than in the case of FIG. 5 (A). FIG. 5 (F) shows the locus of the user's waist when walking on a forehead than in the case of FIG. 5 (A).
 このように、図5(A)から図5(F)までのそれぞれの軌跡は、異なるように見えるが、図3および図4で説明したような特定のパターンを有する。 Thus, although the trajectories from FIG. 5A to FIG. 5F look different, they have a specific pattern as described in FIG. 3 and FIG.
 図6は、この実施の形態において加速度データから算出したユーザの歩行時の腰の軌跡と実測したユーザの歩行時の腰の軌跡との相関を示す図である。図6(A)は、実測したユーザの歩行時の腰の軌跡を歩行の進行方向から見た図である。図6(A)は、図3(B)、図4(B)、および、図5(A)から図5(F)までと同様の図である。 FIG. 6 is a diagram showing the correlation between the user's waist trajectory calculated from acceleration data and the measured user's waist trajectory calculated from the acceleration data in this embodiment. FIG. 6 (A) is a diagram of an actually measured waist trajectory of the user when viewed from the direction of walking. FIG. 6A is a view similar to FIG. 3B, FIG. 4B, and FIG. 5A to FIG. 5F.
 図6(A)の軌跡は、たとえば、ユーザが歩行しているところを、カメラで進行方向から撮影して、画像処理によって、腰の近辺の或る1点の動きを繋ぎ合わせることによって得られる。 The locus shown in FIG. 6A is obtained by, for example, photographing a place where the user is walking from the direction of travel with a camera and connecting the movements of a certain point near the waist by image processing. .
 図6(B)は、加速度データから算出したユーザの歩行時の腰の軌跡を歩行の進行方向から見た図である。ここで、活動量計100の加速度センサによって検出された3軸方向の加速度データに基づいて、ユーザの歩行時の腰の軌跡を算出する方法について説明する。なお、この軌跡は、活動量計100の制御部によって算出される。 FIG. 6 (B) is a view of the hip locus during walking of the user, calculated from the acceleration data, as seen from the direction of walking. Here, a method of calculating the hip locus during walking of the user based on the triaxial acceleration data detected by the acceleration sensor of the activity meter 100 will be described. This locus is calculated by the control unit of the activity meter 100.
 まず、図2で説明したX軸、Y軸およびZ軸方向それぞれの加速度Ax(t),Ay(t),Az(t)を特定する。ここで、加速度センサの3軸方向がX軸、Y軸およびZ軸方向と一致している場合は、加速度センサで得られた検出値をそのままX軸、Y軸およびZ軸方向それぞれの加速度Ax(t),Ay(t),Az(t)とすればよい。一方、加速度センサの3軸方向がX軸、Y軸およびZ軸方向と一致していない場合は、加速度センサで得られた検出値を座標変換することによって、X軸、Y軸およびZ軸方向それぞれの加速度Ax(t),Ay(t),Az(t)を算出する。 First, the accelerations Ax (t), Ay (t), and Az (t) in the X-axis, Y-axis, and Z-axis directions described with reference to FIG. 2 are specified. Here, when the three-axis direction of the acceleration sensor coincides with the X-axis, Y-axis, and Z-axis directions, the detected values obtained by the acceleration sensor are directly used as the accelerations Ax in the X-axis, Y-axis, and Z-axis directions. (T), Ay (t), Az (t) may be used. On the other hand, when the three-axis direction of the acceleration sensor does not coincide with the X-axis, Y-axis, and Z-axis directions, the X-axis, Y-axis, and Z-axis directions are converted by coordinate conversion of the detection values obtained by the acceleration sensor. The respective accelerations Ax (t), Ay (t), Az (t) are calculated.
 次に、式1から式3をそれぞれ用いて加速度Ax(t),Ay(t),Az(t)を積分することによって、X軸、Y軸およびZ軸方向それぞれの速度Vx(t),Vy(t),Vz(t)を算出する。 Next, by integrating the accelerations Ax (t), Ay (t), and Az (t) using Equations 1 to 3, respectively, the velocity Vx (t), X-axis, Y-axis, and Z-axis directions respectively. Vy (t) and Vz (t) are calculated.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
 次いで、式4から式6をそれぞれ用いて、±1歩分の時間の間の短時間での平均速度成分を除外した速度、つまり、短時間での平均速度に対する相対速度Vx'(t),Vy'(t),Vz'(t)を算出する。なお、ここでは、1歩分の時間をT秒とし、たとえば、1歩ごとに加速度のピーク間の時間を算出することによってTが算出される。 Next, using the equations 4 to 6, respectively, the speed excluding the average speed component in the short time during the time of ± 1 step, that is, the relative speed Vx ′ (t), Vy ′ (t) and Vz ′ (t) are calculated. Here, the time for one step is T seconds, and T is calculated, for example, by calculating the time between acceleration peaks for each step.
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000006
 最後に、式7から式9をそれぞれ用いて速度Vx'(t),Vy'(t),Vz'(t)を積分することによって、X軸、Y軸およびZ軸方向それぞれの短時間での平均位置に対する相対位置X(t),Y(t),Z(t)を算出する。 Finally, by integrating the velocities Vx ′ (t), Vy ′ (t), and Vz ′ (t) using Equations 7 to 9, respectively, in the X axis, Y axis, and Z axis directions in a short time. The relative positions X (t), Y (t), and Z (t) with respect to the average position are calculated.
Figure JPOXMLDOC01-appb-M000007
Figure JPOXMLDOC01-appb-M000007
Figure JPOXMLDOC01-appb-M000008
Figure JPOXMLDOC01-appb-M000008
Figure JPOXMLDOC01-appb-M000009
Figure JPOXMLDOC01-appb-M000009
 このようにして算出された位置X(t),Y(t)をそれぞれX,Y座標の値とする点(X(t),Y(t))をtを変化させながらXY平面にプロットしていくことによって、ユーザの歩行時の軌跡をXY平面に投影した軌跡が得られる。この軌跡の一例が、図6(B)に示した軌跡である。 The points (X (t) and Y (t)) having the calculated positions X (t) and Y (t) as X and Y coordinate values are plotted on the XY plane while changing t. Thus, a trajectory obtained by projecting the trajectory of the user on the XY plane can be obtained. An example of this locus is the locus shown in FIG.
 また、位置X(t),Z(t)をそれぞれX,Z座標の値とする点(X(t),Z(t))をtを変化させながらXZ平面にプロットしていくことによって、ユーザの歩行時の軌跡をXZ平面に投影した軌跡が得られる。 Further, by plotting points (X (t), Z (t)) having positions X (t) and Z (t) as X and Z coordinate values on the XZ plane while changing t, A trajectory obtained by projecting a user's walking trajectory onto the XZ plane is obtained.
 同様に、位置Y(t),Z(t)をそれぞれY,Z座標の値とする点(Y(t),Z(t))をtを変化させながらYZ平面にプロットしていくことによって、ユーザの歩行時の軌跡をYZ平面に投影した軌跡が得られる。 Similarly, by plotting points (Y (t), Z (t)) whose positions Y (t) and Z (t) are values of Y and Z coordinates, respectively, on the YZ plane while changing t. A trajectory obtained by projecting the trajectory during walking of the user onto the YZ plane is obtained.
 これらの軌跡は、それぞれ、後述する図7(A)から図9(A)で示すようなパターンの軌跡となる。 These trajectories are traces of patterns as shown in FIGS. 7A to 9A, which will be described later.
 図6(C)は、実測した軌跡の高さ(Y軸方向の幅)と、検出した加速度データから算出した軌跡の高さ(Y軸方向の幅)との相関関係を示すグラフである。このように、様々な歩き方をした場合のそれぞれの高さをプロットする。そして、回帰分析することによって、実測した軌跡の高さをyとして、算出した軌跡の高さをxとして、回帰式がy=0.9878x+0.3452、決定係数R2が0.9575と求められる。 FIG. 6C is a graph showing the correlation between the actually measured height of the trajectory (width in the Y-axis direction) and the height of the trajectory calculated from the detected acceleration data (width in the Y-axis direction). In this way, the respective heights when walking in various ways are plotted. Then, by performing regression analysis, the height of the actually measured trajectory is y, the calculated trajectory height is x, the regression equation is obtained as y = 0.9878x + 0.3452, and the determination coefficient R2 is 0.9575.
 このことから、加速度データから算出した軌跡は、かなり高い精度で、実測した軌跡と一致すると言える。 From this, it can be said that the trajectory calculated from the acceleration data coincides with the actually measured trajectory with considerably high accuracy.
 図7は、この実施の形態においてXY平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。図8は、この実施の形態においてXZ平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。図9は、この実施の形態においてYZ平面に投影した軌跡のパターンに含まれる特徴点を説明するための図である。 FIG. 7 is a diagram for explaining the feature points included in the locus pattern projected on the XY plane in this embodiment. FIG. 8 is a diagram for explaining the feature points included in the locus pattern projected on the XZ plane in this embodiment. FIG. 9 is a diagram for explaining the feature points included in the locus pattern projected on the YZ plane in this embodiment.
 図7を参照して、特徴点(1)は、歩行周期のうち、右足が接地したときの点である。特徴点(1)を特定するための条件は、左右に関しては、右であり、上下に関しては、最も下であるという条件である。 Referring to FIG. 7, feature point (1) is a point when the right foot touches down in the walking cycle. The condition for specifying the feature point (1) is that the right side is the right and the top and the bottom is the lowest.
 特徴点(2)は、歩行周期のうち、右足が立脚しているときの点(特に鉛直方向に最も高い位置にユーザの腰があるときの点)である。特徴点(2)を特定するための条件は、特徴点(1)の後であって、上下に関しては、最も上であるという条件である。 Feature point (2) is a point when the right foot is standing in the walking cycle (particularly a point when the user's waist is at the highest position in the vertical direction). The condition for specifying the feature point (2) is a condition that it is after the feature point (1) and is the highest in the vertical direction.
 特徴点(3)は、歩行周期のうち、左足が接地したときの点である。特徴点(3)を特定するための条件は、特徴点(2)の後であって、上下に関しては、最も下であるという条件である。 Feature point (3) is the point when the left foot touches down in the walking cycle. The condition for specifying the feature point (3) is a condition that it is after the feature point (2) and is the lowest in the vertical direction.
 特徴点(4)は、歩行周期のうち、左足が立脚しているときの点(特に鉛直方向に最も高い位置にユーザの腰があるときの点)である。特徴点(4)を特定するための条件は、特徴点(3)の後であって、上下方向に関しては、最も上であるという条件である。 Feature point (4) is a point when the left foot is standing in the walking cycle (particularly a point when the user's waist is at the highest position in the vertical direction). The condition for specifying the feature point (4) is a condition that it is after the feature point (3) and is the highest in the vertical direction.
 特徴点(5)は、歩行周期のうち、右足が接地したときの点である。特徴点(5)を特定するための条件は、特徴点(4)の後であって、上下に関しては、最も下であるという条件である。なお、この特徴点(5)が、次の1サイクルの特徴点(1)である。 Feature point (5) is the point when the right foot touches down in the walking cycle. The condition for specifying the feature point (5) is the condition that it is after the feature point (4) and is the lowest in the vertical direction. This feature point (5) is the feature point (1) of the next one cycle.
 特徴点(6)は、歩行周期のうち、最も右側にユーザの腰があるときの点である。特徴点(6)を特定するための条件は、式7で算出されたX(t)の値が1サイクルでX(t)≧0において最大であるという条件である。 Feature point (6) is a point when the user's waist is on the rightmost side in the walking cycle. The condition for specifying the feature point (6) is a condition that the value of X (t) calculated by Expression 7 is maximum when X (t) ≧ 0 in one cycle.
 特徴点(7)は、歩行周期のうち、最も左側にユーザの腰があるときの点である。特徴点(7)を特定するための条件は、式7で算出されたX(t)の値が1サイクルでX(t)<0において最小であるという条件である。 Feature point (7) is a point when the user's waist is on the leftmost side in the walking cycle. The condition for specifying the feature point (7) is a condition that the value of X (t) calculated by Expression 7 is minimum in one cycle when X (t) <0.
 特徴点(8)は、歩行周期のうち、歩行1サイクルにおける、腰の軌跡の交点である。特徴点(8)を特定するための条件は、特徴点(2)から特徴点(3)にかける腰の軌跡と、特徴点(4)から特徴点(5)にかける腰の軌跡の、XY平面上における交点であるという条件である。 Feature point (8) is the intersection of the loci of the waist in one walking cycle in the walking cycle. The conditions for specifying the feature point (8) are the XY of the waist locus from the feature point (2) to the feature point (3) and the waist locus from the feature point (4) to the feature point (5). It is a condition that it is an intersection on a plane.
 図8を参照して、特徴点(9)は、歩行周期のうち、右足が接地したときの点である。特徴点(9)を特定するための条件は、左右に関しては、右であり、前後に関しては、最も後ろであるという条件である。 Referring to FIG. 8, the feature point (9) is a point when the right foot touches down in the walking cycle. The condition for specifying the feature point (9) is a condition that the right and left sides are right and the front and rear sides are the rearmost.
 特徴点(10)は、歩行周期のうち、右足が立脚しているときの点(特に進行方向の短時間での平均位置に対する相対位置が最も前にユーザの腰があるときの点)である。特徴点(10)を特定するための条件は、特徴点(9)の後であって、前後に関しては、最も前であるという条件である。 The feature point (10) is a point when the right foot is standing in the walking cycle (particularly a point when the user's waist is at the foremost position relative to the average position in a short time in the traveling direction). . The condition for specifying the feature point (10) is a condition that it is after the feature point (9) and is the foremost in terms of front and rear.
 特徴点(11)は、歩行周期のうち、左足が接地したときの点である。特徴点(11)を特定するための条件は、特徴点(10)の後であって、前後に関しては、最も後ろであるという条件である。 Feature point (11) is the point when the left foot touches down in the walking cycle. The condition for specifying the feature point (11) is a condition that it is after the feature point (10) and is at the back of the front and rear.
 特徴点(12)は、歩行周期のうち、左足が立脚しているときの点(特に進行方向の短時間での平均位置に対する相対位置が最も前にユーザの腰があるときの点)である。特徴点(12)を特定するための条件は、特徴点(11)の後であって、前後に関しては、最も前であるという条件である。 The feature point (12) is a point when the left foot is standing in the walking cycle (particularly a point when the user's waist is at the forefront relative position to the average position in a short time in the traveling direction). . The condition for specifying the feature point (12) is a condition that it is after the feature point (11) and is the foremost in terms of front and rear.
 特徴点(13)は、歩行周期のうち、右足が接地したときの点である。特徴点(11)を特定するための条件は、特徴点(12)の後であって、前後に関しては、最も後ろであるという条件である。なお、この特徴点(13)が、次の1サイクルの特徴点(9)である。 Feature point (13) is the point when the right foot touches down in the walking cycle. The condition for specifying the feature point (11) is a condition that it is after the feature point (12) and is at the back of the front and rear. This feature point (13) is the feature point (9) of the next one cycle.
 特徴点(14)は、歩行周期のうち、歩行1サイクルにおける、腰の軌跡の交点である。特徴点(14)を特定するための条件は、特徴点(10)から特徴点(11)にかける腰の軌跡と、特徴点(12)から特徴点(13)にかける腰の軌跡の、XY平面上における交点であるという条件である。 Feature point (14) is the intersection of the loci of the waist in one walking cycle in the walking cycle. The conditions for specifying the feature point (14) are the XY of the waist locus from the feature point (10) to the feature point (11) and the waist locus from the feature point (12) to the feature point (13). It is a condition that it is an intersection on a plane.
 図9を参照して、図7で説明した特徴点(1),(3),(5)は、それぞれ、YZ平面に投影した軌跡のパターンのうち最も下の点となる。また、特徴点(2),(4)は、それぞれ、YZ平面に投影した軌跡のパターンのうち最も上の点となる。 Referring to FIG. 9, feature points (1), (3), and (5) described in FIG. 7 are the lowest points in the locus pattern projected on the YZ plane. The feature points (2) and (4) are the uppermost points in the locus pattern projected on the YZ plane.
 図10は、この実施の形態においてXY平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。図11は、この実施の形態においてXZ平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。図12は、この実施の形態においてYZ平面に投影した軌跡のパターンに含まれる特徴点の位置に基づいて算出される特徴因子を説明するための図である。 FIG. 10 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the XY plane in this embodiment. FIG. 11 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the XZ plane in this embodiment. FIG. 12 is a diagram for explaining the feature factor calculated based on the position of the feature point included in the locus pattern projected on the YZ plane in this embodiment.
 図10を参照して、特徴因子Wuは、XY平面における特徴点(2)と特徴点(4)との間のX軸方向の距離(「上側左右幅」という)であり、特徴点(2)のX座標の値から特徴点(4)のX座標の値を引くことで算出される。 Referring to FIG. 10, the feature factor Wu is a distance in the X-axis direction (referred to as “upper left / right width”) between the feature point (2) and the feature point (4) on the XY plane, and the feature point (2 ) Is subtracted from the X coordinate value of the feature point (4).
 特徴因子Wdは、XY平面における特徴点(1)と特徴点(3)との間のX軸方向の距離(「下側左右幅」という)であり、特徴点(1)のX座標の値から特徴点(3)のX座標の値を引くことで算出される。 The feature factor Wd is a distance in the X-axis direction between the feature point (1) and the feature point (3) on the XY plane (referred to as “lower left / right width”), and the value of the X coordinate of the feature point (1). Is calculated by subtracting the X-coordinate value of the feature point (3).
 特徴因子Wは、XY平面における特徴点(6)と特徴点(7)との間のX軸方向の距離(「左右幅」という)であり、特徴点(6)のX座標の値から特徴点(7)のX座標の値を引くことで算出される。 The feature factor W is a distance in the X-axis direction between the feature point (6) and the feature point (7) on the XY plane (referred to as “left-right width”). It is calculated by subtracting the X coordinate value of the point (7).
 特徴因子Hlは、XY平面における特徴点(4)と特徴点(3)との間のY軸方向の距離(「左側上下幅」という)であり、特徴点(4)のY座標の値から特徴点(3)のY座標の値を引くことで算出される。 The feature factor H1 is a distance in the Y-axis direction between the feature point (4) and the feature point (3) on the XY plane (referred to as “left-side vertical width”), and is obtained from the value of the Y coordinate of the feature point (4). It is calculated by subtracting the Y coordinate value of the feature point (3).
 特徴因子Hrは、XY平面における特徴点(2)と特徴点(1)との間のY軸方向の距離(「右側上下幅」という)であり、特徴点(2)のY座標の値から特徴点(1)のY座標の値を引くことで算出される。 The feature factor Hr is a distance in the Y-axis direction between the feature point (2) and the feature point (1) on the XY plane (referred to as “right upper and lower width”), and is obtained from the value of the Y coordinate of the feature point (2). It is calculated by subtracting the Y coordinate value of the feature point (1).
 特徴因子Hは、XY平面における特徴因子Hlと特徴因子Hrとの平均(「上下幅」という)であり、HlとHrとを足して2で割ることで算出される。 Feature factor H is the average of feature factor H1 and feature factor Hr on the XY plane (referred to as “vertical width”), and is calculated by adding H1 and Hr and dividing by two.
 特徴因子Hclは、XY平面における特徴点(3)を基準とした特徴点(8)の高さ(「左側クロス点高さ)であり、特徴点(8)のY座標の値から特徴点(3)のY座標の値を引くことで算出される。 The feature factor Hcl is the height (“left cross point height) of the feature point (8) on the basis of the feature point (3) on the XY plane. The feature point (8) is obtained from the Y coordinate value of the feature point (8). It is calculated by subtracting the Y coordinate value of 3).
 特徴因子Hcrは、XY平面における特徴点(1)を基準とした特徴点(8)の高さ(「右側クロス点高さ)であり、特徴点(8)のY座標の値から特徴点(1)のY座標の値を引くことで算出される。 The feature factor Hcr is the height of the feature point (8) with respect to the feature point (1) on the XY plane (“right cross point height), and the feature point (8) is obtained from the Y coordinate value of the feature point (8). It is calculated by subtracting the Y coordinate value of 1).
 特徴因子ISOは、XY平面における軌跡の上下幅に対する、特徴点(8)の高さ(「位相」という)であり、特徴因子Hclを特徴因子Hlで割ったものと、特徴因子Hcrを特徴因子Hrで割ったものとを足して2で割ることで算出される。 The feature factor ISO is the height (referred to as “phase”) of the feature point (8) with respect to the vertical width of the trajectory in the XY plane. The feature factor Hcl is divided by the feature factor Hl, and the feature factor Hcr is the feature factor. Calculated by adding to Hr and dividing by 2.
 特徴因子Vlevは、XY平面における軌跡の上側が開いているのか、下側が開いているのかの度合(「形状∨or∧」という)であり、特徴因子Wuを特徴因子Wdで割ることで算出される。 The feature factor Vlev is the degree of whether the upper side of the trajectory in the XY plane is open or lower (called “shape ∨ or ∧”), and is calculated by dividing the feature factor Wu by the feature factor Wd. The
 特徴因子Ilevは、XY平面における軌跡の形状が、縦長の形状であるのか、横長の形状であるのかを特定するための因子(「形状I」という)であり、特徴因子Hを特徴因子Wで割ることで算出される。 The feature factor Ilev is a factor (referred to as “shape I”) for specifying whether the shape of the locus on the XY plane is a vertically long shape or a horizontally long shape. Calculated by dividing.
 特徴因子Hbは、XY平面における左右の上下幅の比(「左右上下幅比」という)であり、特徴因子Hrを特徴因子Hlで割ることで算出される。 The characteristic factor Hb is a ratio of the left and right vertical widths in the XY plane (referred to as “left / right vertical width ratio”), and is calculated by dividing the characteristic factor Hr by the characteristic factor Hl.
 特徴因子Ybは、XY平面における左右の高さの比(「左右高さ比」という)であり、特徴点(4)のY座標の値と特徴点(1)のY座標の値との差を、特徴点(2)のY座標の値と特徴点(3)のY座標の値との差で割ることで算出される。 The feature factor Yb is the ratio of the left and right heights on the XY plane (referred to as “left / right height ratio”), and the difference between the Y coordinate value of the feature point (4) and the Y coordinate value of the feature point (1). Is divided by the difference between the Y coordinate value of the feature point (2) and the Y coordinate value of the feature point (3).
 特徴因子Wbは、XY平面における左右の幅の比(「左右幅の比」という)であり、特徴点(6)のX座標の値と特徴点(8)のX座標の値との差を、特徴点(8)のX座標の値と特徴点(7)のY座標の値との差で割ることで算出される。 The feature factor Wb is a ratio of the left and right widths on the XY plane (referred to as “right / left width ratio”), and the difference between the X coordinate value of the feature point (6) and the X coordinate value of the feature point (8). , By dividing by the difference between the X coordinate value of the feature point (8) and the Y coordinate value of the feature point (7).
 特徴因子Stlは、XY平面における右足が接地してから左足が接地するまでの上下振幅の合計(「右足接地から左足接地までの上下振幅」という)であり、特徴点(2)のY座標の値から特徴点(1)のY座標の値を引いたものと、特徴点(2)のY座標の値から特徴点(3)のY座標の値を引いたものとを足すことで算出される。 The characteristic factor St1 is the sum of the vertical amplitudes (referred to as “vertical amplitude from the right foot grounding to the left foot grounding”) until the left foot touches the ground on the XY plane. This is calculated by adding the value obtained by subtracting the Y coordinate value of the feature point (1) and the value obtained by subtracting the Y coordinate value of the feature point (3) from the Y coordinate value of the feature point (2). The
 特徴因子Strは、XY平面における左足が接地してから右足が接地するまでの上下振幅の合計(「左足接地から右足接地までの上下振幅」という)であり、特徴点(4)のY座標の値から特徴点(3)のY座標の値を引いたものと、特徴点(4)のY座標の値から特徴点(5)のY座標の値を引いたものとを足すことで算出される。 The characteristic factor Str is a sum of vertical amplitudes from the left foot to the ground on the XY plane until the right foot contacts the ground (referred to as “vertical amplitude from the left foot grounding to the right foot grounding”). Calculated by adding the value obtained by subtracting the Y coordinate value of the feature point (3) and the value obtained by subtracting the Y coordinate value of the feature point (5) from the Y coordinate value of the feature point (4) The
 特徴因子junは、軌跡が時計回りに書かれているか反時計回りに書かれているかを示す因子(「書き順」という)であり、特徴点(2)と特徴点(4)のX座標の正負判定を行なうことで算出される。 The feature factor “jun” is a factor (referred to as “writing order”) indicating whether the locus is written clockwise or counterclockwise, and the X coordinate of the feature point (2) and the feature point (4) It is calculated by making a positive / negative determination.
 図11を参照して、特徴因子WuSuは、XZ平面における特徴点(10)と特徴点(12)との間のX軸方向の距離(「上側左右幅」という)であり、特徴点(10)のX座標の値から特徴点(12)のX座標の値を引くことで算出される。 Referring to FIG. 11, the feature factor WuSu is a distance in the X-axis direction (referred to as “upper left-right width”) between the feature point (10) and the feature point (12) on the XZ plane, and the feature point (10 ) Is subtracted from the X coordinate value of the feature point (12).
 特徴因子WdSuは、XZ平面における特徴点(9)と特徴点(11)との間のX軸方向の距離(「下側左右幅」という)であり、特徴点(9)のX座標の値から特徴点(11)のX座標の値を引くことで算出される。 The feature factor WdSu is the distance in the X-axis direction between the feature point (9) and the feature point (11) on the XZ plane (referred to as “lower left / right width”), and the value of the X coordinate of the feature point (9) Is calculated by subtracting the X-coordinate value of the feature point (11).
 特徴因子Wsuは、XZ平面における特徴点(6)と特徴点(7)との間のX軸方向の距離(「左右幅」という)であり、特徴点(6)のX座標の値から特徴点(7)のX座標の値を引くことで算出される。 The feature factor Wsu is a distance in the X-axis direction (referred to as “horizontal width”) between the feature point (6) and the feature point (7) on the XZ plane. The feature factor Wsu is a feature based on the X coordinate value of the feature point (6). It is calculated by subtracting the X coordinate value of the point (7).
 特徴因子HlSuは、XZ平面における特徴点(12)と特徴点(11)との間のZ軸方向の距離(「左側上下幅」という)であり、特徴点(12)のZ座標の値から特徴点(11)のZ座標の値を引くことで算出される。 The feature factor HlSu is a distance in the Z-axis direction between the feature point (12) and the feature point (11) in the XZ plane (referred to as “left-side vertical width”), and is obtained from the value of the Z coordinate of the feature point (12). It is calculated by subtracting the Z coordinate value of the feature point (11).
 特徴因子HrSuは、XZ平面における特徴点(10)と特徴点(9)との間のZ軸方向の距離(「右側上下幅」という)であり、特徴点(10)のZ座標の値から特徴点(9)のZ座標の値を引くことで算出される。 The feature factor HrSu is a distance in the Z-axis direction between the feature point (10) and the feature point (9) in the XZ plane (referred to as “right upper and lower width”), and is obtained from the value of the Z coordinate of the feature point (10). It is calculated by subtracting the Z coordinate value of the feature point (9).
 特徴因子Hsuは、XZ平面における特徴因子HlSuと特徴因子HrSuとの平均(「上下幅」という)であり、HlSuとHrSuとを足して2で割ることで算出される。 The feature factor Hsu is an average of the feature factor HlSu and the feature factor HrSu on the XZ plane (referred to as “vertical width”), and is calculated by adding HlSu and HrSu and dividing by two.
 特徴因子HclSuは、XZ平面における特徴点(11)を基準とした特徴点(8)の高さ(「左側クロス点高さ)であり、特徴点(8)のZ座標の値から特徴点(11)のZ座標の値を引くことで算出される。 The feature factor HclSu is the height (“left cross point height) of the feature point (8) on the basis of the feature point (11) in the XZ plane. The feature point (8) is obtained from the Z-coordinate value of the feature point (8). 11) is calculated by subtracting the value of the Z coordinate.
 特徴因子HcrSuは、XZ平面における特徴点(9)を基準とした特徴点(8)の高さ(「右側クロス点高さ)であり、特徴点(8)のZ座標の値から特徴点(9)のZ座標の値を引くことで算出される。 The feature factor HcrSu is the height (“right cross point height) of the feature point (8) on the basis of the feature point (9) in the XZ plane. The feature point (8) is obtained from the Z coordinate value of the feature point (8) ( It is calculated by subtracting the Z coordinate value of 9).
 特徴因子ISOSuは、XY平面における軌跡の上下幅に対する、特徴点(14)の高さ(「位相」という)であり、図10で説明したXY平面のISOと同じ値である。 The feature factor ISOSu is the height (referred to as “phase”) of the feature point (14) with respect to the vertical width of the trajectory on the XY plane, and is the same value as the ISO on the XY plane described in FIG.
 特徴因子VlevSuは、XZ平面における軌跡の上側が開いているのか、下側が開いているのかの度合(「形状∨or∧」という)であり、特徴因子WuSuを特徴因子WdSuで割ることで算出される。 The feature factor VlevSu is the degree of whether the upper side of the trajectory in the XZ plane is open or lower (referred to as “shape ∨ or 、”), and is calculated by dividing the feature factor WuSu by the feature factor WdSu. The
 特徴因子IlevSuは、XZ平面における軌跡の形状が、縦長の形状であるのか、横長の形状であるのかを特定するための因子(「形状I」という)であり、特徴因子Hsuを特徴因子Wsuで割ることで算出される。 The feature factor IlevSu is a factor (referred to as “shape I”) for specifying whether the shape of the trajectory in the XZ plane is a vertically long shape or a horizontally long shape. The feature factor Hsu is a feature factor Wsu. Calculated by dividing.
 特徴因子HbSuは、XZ平面における左右の上下幅の比(「左右上下幅比」という)であり、特徴因子HrSuを特徴因子HlSuで割ることで算出される。 The characteristic factor HbSu is a ratio of the left and right vertical widths in the XZ plane (referred to as “left / right vertical width ratio”), and is calculated by dividing the characteristic factor HrSu by the characteristic factor HlSu.
 特徴因子YbSuは、XZ平面における左右の高さの比(「左右高さ比」という)であり、特徴点(13)のZ座標の値と特徴点(9)のZ座標の値との差を、特徴点(10)のZ座標の値と特徴点(11)のZ座標の値との差で割ることで算出される。 The feature factor YbSu is the ratio of the left and right heights in the XZ plane (referred to as “left-right height ratio”), and the difference between the Z coordinate value of the feature point (13) and the Z coordinate value of the feature point (9). Is divided by the difference between the Z coordinate value of the feature point (10) and the Z coordinate value of the feature point (11).
 特徴因子WbSuは、XZ平面における左右の幅の比(「左右幅の比」という)であり、図10で説明したXY平面のWbと同じ値である。 The characteristic factor WbSu is a ratio of the left and right widths in the XZ plane (referred to as “right / left width ratio”), and is the same value as Wb of the XY plane described in FIG.
 特徴因子StlSuは、XZ平面における右足が接地してから左足が接地するまでの前後振幅の合計(「右足接地から左足接地までの前後振幅」という)であり、特徴点(10)のZ座標の値から特徴点(9)のZ座標の値を引いたものと、特徴点(10)のZ座標の値から特徴点(11)のZ座標の値を引いたものとを足すことで算出される。 The characteristic factor StlSu is the sum of the front and rear amplitudes (referred to as “front and rear amplitudes from the right foot grounding to the left foot grounding”) until the left foot touches the ground in the XZ plane, and is the Z coordinate of the feature point (10). It is calculated by adding the value obtained by subtracting the Z coordinate value of the feature point (9) and the value obtained by subtracting the Z coordinate value of the feature point (11) from the Z coordinate value of the feature point (10). The
 特徴因子StrSuは、XZ平面における左足が接地してから右足が接地するまでの前後振幅の合計(「左足接地から右足接地までの前後振幅」という)であり、特徴点(12)のZ座標の値から特徴点(11)のZ座標の値を引いたものと、特徴点(12)のZ座標の値から特徴点(13)のZ座標の値を引いたものとを足すことで算出される。 The characteristic factor StrSu is the sum of the front and rear amplitudes (referred to as “front and rear amplitudes from the left foot contact to the right foot contact”) until the right foot contacts the ground in the XZ plane, and the Z coordinate of the feature point (12) It is calculated by adding the value obtained by subtracting the Z coordinate value of the feature point (11) and the value obtained by subtracting the Z coordinate value of the feature point (13) from the Z coordinate value of the feature point (12). The
 特徴因子Zflは、XZ平面における左足が立脚中で腰の位置が最上点に達してから前後に移動した幅(「左足立脚の最上点からの腰の前後移動」という)であり、特徴点(12)のZ座標の値から特徴点(4)のZ座標の値を引くことで算出される。 The characteristic factor Zfl is a width in which the left foot in the XZ plane is standing and moves back and forth after the position of the waist reaches the highest point (referred to as “backward movement of the waist from the uppermost point of the left stepped leg”). It is calculated by subtracting the Z coordinate value of the feature point (4) from the Z coordinate value of 12).
 特徴因子Zfrは、XZ平面における右足が立脚中で腰の位置が最上点に達してから前後に移動した幅(「右足立脚の最上点からの腰の前後移動」という)であり、特徴点(10)のZ座標の値から特徴点(2)のZ座標の値を引くことで算出される。 The characteristic factor Zfr is a width (referred to as “backward movement of the waist from the uppermost point of the right foot stand”) when the right foot is standing on the XZ plane and the position of the waist reaches the uppermost point and then moved back and forth. It is calculated by subtracting the Z coordinate value of the feature point (2) from the Z coordinate value of 10).
 特徴因子Zfは、XZ平面における立脚中で腰の位置が最上点に達してから前後に移動した幅(「立脚の最上点からの腰の前後移動」という)であり、特徴因子Zflと特徴因子Zfrとを足して2で割ることで算出される。 The characteristic factor Zf is a width that moves back and forth after the position of the waist reaches the highest point in the stance on the XZ plane (referred to as “backward movement of the waist from the highest point of the stance”), and the characteristic factor Zfl and the characteristic factor Calculated by adding Zfr and dividing by 2.
 特徴因子Zblは、XZ平面における左足が接地してから腰の位置が前後に移動した幅(「左足接地からの腰の前後移動」という)であり、特徴点(11)のZ座標の値から特徴点(3)のZ座標の値を引くことで算出される。 The characteristic factor Zbl is a width (referred to as “backward movement of the waist from the left foot contact”) after the left foot touches the ground in the XZ plane (referred to as “backward movement of the waist from the left foot contact”). It is calculated by subtracting the Z coordinate value of the feature point (3).
 特徴因子Zbrは、XZ平面における右足が接地してから腰の位置が前後に移動した幅(「右足接地からの腰の前後移動」という)であり、特徴点(9)のZ座標の値から特徴点(5)のZ座標の値を引くことで算出される。 The feature factor Zbr is a width (referred to as “backward back-and-forth movement from the right foot grounding”) after the right foot in the XZ plane touches the ground, and from the value of the Z coordinate of the feature point (9). It is calculated by subtracting the Z coordinate value of the feature point (5).
 特徴因子Zbは、XZ平面における足が接地してから腰の位置が前後に移動した幅(「接地からの腰の前後移動」という)であり、特徴因子Zblと特徴因子Zbrとを足して2で割ることで算出される。 The feature factor Zb is a width in which the position of the waist has moved back and forth after the foot in the XZ plane contacts the ground (referred to as “backward movement of the waist from the ground”), and the feature factor Zbl and the feature factor Zbr are 2 Calculated by dividing by.
 図12を参照して、特徴因子dZは、YZ平面における前後の傾き(「前後の傾き」という)であり、特徴点(2)のY座標の値から特徴点(1)のY座標の値を引いたものを、特徴点(2)のZ座標の値から特徴点(1)のZ座標の値を引いたもので割ることで算出される。 Referring to FIG. 12, the feature factor dZ is a forward / backward tilt (referred to as “front / back tilt”) in the YZ plane, and the value of the Y coordinate of the feature point (1) from the value of the Y coordinate of the feature point (2). Is divided by the value obtained by subtracting the value of the Z coordinate of the feature point (1) from the value of the Z coordinate of the feature point (2).
 特徴因子StlShiは、YZ平面における左の斜め方向の振幅の合計(「左の前後振幅」という)であり、YZ平面における特徴点(2)および特徴点(1)の距離と、YZ平面における特徴点(2)および特徴点(3)の距離とを足すことで算出される。YZ平面における特徴点(2)および特徴点(1)の距離は、特徴点(2)のZ座標の値から特徴点(1)のZ座標の値を引いたものの2乗と、特徴点(2)のY座標の値から特徴点(1)のY座標の値を引いたものの2乗とを足したものの平方根として算出される。YZ平面における特徴点(2)および特徴点(3)の距離は、特徴点(2)のZ座標の値から特徴点(3)のZ座標の値を引いたものの2乗と、特徴点(2)のY座標の値から特徴点(3)のY座標の値を引いたものの2乗とを足したものの平方根として算出される。 The characteristic factor StlShi is the sum of the amplitudes in the left diagonal direction in the YZ plane (referred to as “left front-rear amplitude”), the distance between the characteristic points (2) and (1) in the YZ plane, and the characteristics in the YZ plane. It is calculated by adding the distance between the point (2) and the feature point (3). The distance between the feature point (2) and the feature point (1) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (1) from the Z coordinate value of the feature point (2), and the feature point ( It is calculated as the square root of the sum of the value obtained by subtracting the Y coordinate value of the feature point (1) and the square of the Y coordinate value of 2). The distance between the feature point (2) and the feature point (3) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (3) from the Z coordinate value of the feature point (2), and the feature point ( It is calculated as the square root of the sum of the value obtained by subtracting the Y coordinate value of the feature point (3) and the square of the Y coordinate value of 2).
 特徴因子StrShiは、YZ平面における右の斜め方向の振幅の合計(「右の前後振幅」という)であり、YZ平面における特徴点(4)および特徴点(3)の距離と、YZ平面における特徴点(4)および特徴点(1)の距離とを足すことで算出される。YZ平面における特徴点(4)および特徴点(3)の距離は、特徴点(4)のZ座標の値から特徴点(3)のZ座標の値を引いたものの2乗と、特徴点(4)のY座標の値から特徴点(3)のY座標の値を引いたものの2乗とを足したものの平方根として算出される。YZ平面における特徴点(4)および特徴点(1)の距離は、特徴点(4)のZ座標の値から特徴点(1)のZ座標の値を引いたものの2乗と、特徴点(4)のY座標の値から特徴点(1)のY座標の値を引いたものの2乗とを足したものの平方根として算出される。 The feature factor StrShi is the sum of the amplitudes in the right diagonal direction in the YZ plane (referred to as “right front-rear amplitude”), the distance between the feature points (4) and (3) in the YZ plane, and the features in the YZ plane. It is calculated by adding the distance between the point (4) and the feature point (1). The distance between the feature point (4) and the feature point (3) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (3) from the Z coordinate value of the feature point (4), and the feature point ( It is calculated as the square root of the sum of the square of the value obtained by subtracting the Y coordinate value of the feature point (3) from the Y coordinate value of 4). The distance between the feature point (4) and the feature point (1) on the YZ plane is the square of the value obtained by subtracting the Z coordinate value of the feature point (1) from the Z coordinate value of the feature point (4), and the feature point ( It is calculated as the square root of the sum of the square of the value obtained by subtracting the Y coordinate value of the feature point (1) from the Y coordinate value of 4).
 特徴因子StShiは、YZ平面における斜め方向の振幅の合計(「前後振幅」という)であり、特徴因子StlShiと特徴因子StrShiとを足して2で割ることで算出される。 The feature factor StShi is the sum of the amplitudes in the oblique direction in the YZ plane (referred to as “front-rear amplitude”), and is calculated by adding the feature factor StlShi and the feature factor StrShi and dividing by two.
 図13は、この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩幅との相関関係を説明するための第1の図である。図14は、この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩幅との相関関係を説明するための第2の図である。 FIG. 13 is a first diagram for explaining the correlation between the feature factor and the step length of the index indicating the walking posture in this embodiment. FIG. 14 is a second diagram for illustrating the correlation between the feature factor and the stride among the indices indicating the walking posture in this embodiment.
 図13を参照して、図10で説明したXZ平面に投影した軌跡のパターンの特徴因子Hrを縦軸(y)とし、歩行姿勢を示す指標の1つである歩幅の実際に測定した値を横軸(x)として、データをプロットする。そして、回帰分析することによって、回帰式がy=0.0735x-41.271、決定係数R2が0.7938と求められる。 Referring to FIG. 13, the characteristic factor Hr of the locus pattern projected on the XZ plane described in FIG. 10 is the vertical axis (y), and the actually measured value of the stride, which is one of the indices indicating the walking posture, is obtained. Data is plotted on the horizontal axis (x). Then, by regression analysis, the regression equation is obtained as y = 0.0735x−41.271, and the determination coefficient R2 is determined as 0.7938.
 図14を参照して、図12で説明したYZ平面に投影した軌跡のパターンの特徴因子StShiを縦軸(y)とし、歩行姿勢を示す指標の1つである歩幅の実際に測定した値を横軸(x)として、データをプロットする。そして、回帰分析することによって、回帰式がy=0.1485x-78.963、決定係数R2が0.8192と求められる。 Referring to FIG. 14, the characteristic factor StShi of the locus pattern projected on the YZ plane described in FIG. 12 is taken as the vertical axis (y), and the actually measured value of the stride, which is one of the indices indicating the walking posture, is obtained. Data is plotted on the horizontal axis (x). Then, by regression analysis, the regression equation is obtained as y = 0.1485x−78.963, and the determination coefficient R2 is obtained as 0.8192.
 このように、歩行姿勢を示す指標である歩幅は、特徴因子Hrおよび特徴因子StShiと相関性が高いので、重回帰分析することによって、目的変数としての特徴因子Hrおよび特徴因子StShi、ならびに、説明変数としての歩幅の値との重回帰式である歩幅Length=α×Hr+β×StShi+γの式で歩幅の値を算出することができる。なお、α,β,γは、重回帰分析によって得られる偏回帰係数である。 As described above, the stride, which is an index indicating the walking posture, has a high correlation with the feature factor Hr and the feature factor StShi. Therefore, by performing multiple regression analysis, the feature factor Hr and the feature factor StShi as objective variables and the explanation are explained. The stride value can be calculated by the formula of stride Length = α × Hr + β × StShi + γ, which is a multiple regression equation with the stride value as a variable. Α, β, and γ are partial regression coefficients obtained by multiple regression analysis.
 図15は、この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩隔との相関関係を説明するための第1の図である。図16は、この実施の形態における特徴因子と歩行姿勢を示す指標のうち歩隔との相関関係を説明するための第2の図である。 FIG. 15 is a first diagram for explaining the correlation between the feature factor and the walking distance among the indices indicating the walking posture in this embodiment. FIG. 16 is a second diagram for explaining the correlation between the feature factor and the step indicating the walking posture in this embodiment.
 図15を参照して、図10で説明したXZ平面に投影した軌跡のパターンの特徴因子Hrを特徴因子Wで割った特徴因子Hr/Wを縦軸(y)とし、歩行姿勢を示す指標の1つである歩隔の実際に測定した値を横軸として、データをプロットする。そして、回帰分析することによって、回帰式がy=0.0033x-1.4056、決定係数R2が0.0932と求められる。 Referring to FIG. 15, the characteristic factor Hr / W obtained by dividing the characteristic factor Hr of the locus pattern projected on the XZ plane described in FIG. 10 by the characteristic factor W is taken as the vertical axis (y), and the index indicating the walking posture is shown. Data is plotted with the horizontal axis representing the actually measured value of one step. Then, by regression analysis, the regression equation is obtained as y = 0.0034x-1.4056, and the determination coefficient R2 is determined as 0.0932.
 図14を参照して、図11で説明したXZ平面に投影した軌跡のパターンの特徴因子WuSuを特徴因子WdSuで割った特徴因子WuSu/WdSuを縦軸(y)とし、歩行姿勢を示す指標の1つである歩隔の実際に測定した値を横軸(x)として、データをプロットする。そして、回帰分析することによって、回帰式がy=0.2309x-4.0927、決定係数R2が0.1861と求められる。 Referring to FIG. 14, the characteristic factor WuSu / WdSu obtained by dividing the characteristic factor WuSu of the trajectory pattern projected on the XZ plane described in FIG. 11 by the characteristic factor WdSu is the vertical axis (y), and the index indicating the walking posture Data is plotted with the horizontal axis (x) being the actually measured value of one step. Then, by performing regression analysis, the regression equation is obtained as y = 0.309x−4.0927, and the determination coefficient R2 is obtained as 0.1861.
 また、歩行姿勢を示す指標である歩隔は、重回帰分析することによって、目的変数としての特徴因子Hr/Wおよび特徴因子WuSu/WdSu、ならびに、説明変数としての歩幅の値との重回帰式である歩隔Width=δ×Hr/W+ε×WuSu/WdSu+ζの式で歩隔の値を算出することができる。なお、δ,ε,ζは、重回帰分析によって得られる係数である。 Further, the step, which is an index indicating the walking posture, is subjected to a multiple regression analysis with a multiple regression analysis of the characteristic factor Hr / W and the characteristic factor WuSu / WdSu as objective variables and the stride value as an explanatory variable. Step width Width = δ × Hr / W + ε × WuSu / WdSu + ζ can be used to calculate the step value. Note that δ, ε, and ζ are coefficients obtained by multiple regression analysis.
 図17は、この実施の形態における活動量計100の構成の概略を示すブロック図である。図17を参照して、活動量計100は、制御部110と、メモリ120と、操作部130と、表示部140と、加速度センサ170と、電源190とを含む。また、活動量計100は、音を出力する報音部や外部のコンピュータと通信するためのインターフェイスを含むようにしてもよい。 FIG. 17 is a block diagram showing an outline of the configuration of the activity meter 100 in this embodiment. Referring to FIG. 17, activity meter 100 includes a control unit 110, a memory 120, an operation unit 130, a display unit 140, an acceleration sensor 170, and a power source 190. Further, the activity meter 100 may include a sound report unit for outputting sound and an interface for communicating with an external computer.
 制御部110、メモリ120、操作部130、表示部140、加速度センサ170、および、電源190は、図1で説明した本体部191に内蔵される。 The control unit 110, the memory 120, the operation unit 130, the display unit 140, the acceleration sensor 170, and the power source 190 are incorporated in the main body unit 191 described with reference to FIG.
 操作部130は、図1で説明した表示切換/決定スイッチ131、左操作/メモリスイッチ132、および、右操作スイッチ133を含み、これらのスイッチが操作されたことを示す操作信号を制御部110に送信する。 The operation unit 130 includes the display change / decision switch 131, the left operation / memory switch 132, and the right operation switch 133 described with reference to FIG. 1, and an operation signal indicating that these switches have been operated is sent to the control unit 110. Send.
 加速度センサ170は、MEMS(Micro Electro Mechanical Systems)技術の半導体式のものが用いられるが、これに限定されず、機械式または光学式など他の方式のものであってもよい。加速度センサ170は、本実施の形態においては、3軸方向それぞれの加速度を示す検出信号を制御部110に出力する。しかし、加速度センサ170は、3軸のものに限定されず、1軸または2軸のものであってもよい。 The acceleration sensor 170 is a semiconductor type of MEMS (Micro Electro Mechanical Systems) technology, but is not limited to this, and may be of another type such as a mechanical type or an optical type. In the present embodiment, acceleration sensor 170 outputs a detection signal indicating the acceleration in each of the three axial directions to control unit 110. However, the acceleration sensor 170 is not limited to the three-axis type, and may be one-axis or two-axis type.
 メモリ120は、ROM(Read Only Memory)(たとえば、フラッシュメモリ)などの不揮発性メモリおよびRAM(Random Access Memory)(たとえば、SDRAM(synchronous Dynamic Random Access Memory))などの揮発性メモリを含む。 The memory 120 includes non-volatile memory such as ROM (Read Only Memory) (for example, flash memory) and volatile memory such as RAM (Random Access Memory) (for example, SDRAM (synchronous Dynamic Random Access Memory)).
 メモリ120は、活動量計100を制御するためのプログラムのデータ、活動量計100を制御するために用いられるデータ、活動量計100の各種機能を設定するための設定データ、および、歩数や活動量などの所定時間ごと(たとえば日ごと)の測定結果のデータなどを記憶する。また、メモリ120は、プログラムが実行されるときのワークメモリなどとして用いられる。 The memory 120 includes program data for controlling the activity meter 100, data used for controlling the activity meter 100, setting data for setting various functions of the activity meter 100, and the number of steps and activities. Measurement result data such as quantity is stored every predetermined time (for example, every day). The memory 120 is used as a work memory when the program is executed.
 制御部110は、CPU(Central Processing Unit)を含み、メモリ120に記憶された活動量計100を制御するためのプログラムに従って、操作部130からの操作信号に応じて、加速度センサ170および気圧センサ180からの検出信号に基づいて、メモリ120、および、表示部140を制御する。 The control unit 110 includes a CPU (Central Processing Unit), and according to an operation signal from the operation unit 130 according to a program for controlling the activity meter 100 stored in the memory 120, the acceleration sensor 170 and the atmospheric pressure sensor 180. The memory 120 and the display unit 140 are controlled on the basis of the detection signal from.
 表示部140は、図1で説明したディスプレイ141を含み、制御部110からの制御信号に従った所定の情報を、ディスプレイ141に表示するよう制御する。 The display unit 140 includes the display 141 described with reference to FIG. 1 and controls the display 141 to display predetermined information according to a control signal from the control unit 110.
 電源190は、取替可能な電池を含み、電池からの電力を活動量計100の制御部110などの動作するのに電力が必要な各部に供給する。 The power source 190 includes a replaceable battery, and supplies power from the battery to each unit that requires power to operate, such as the control unit 110 of the activity meter 100.
 図18は、この実施の形態における活動量計100の機能の概略を示す機能ブロック図である。図18を参照して、活動量計100の制御部110は、加速度読込制御部111と、特徴点位置特定部112と、特徴因子算出部113と、指標算出部114と、歩行姿勢判定部115と、表示制御部116とを含む。 FIG. 18 is a functional block diagram showing an outline of the function of the activity meter 100 in this embodiment. Referring to FIG. 18, the control unit 110 of the activity meter 100 includes an acceleration reading control unit 111, a feature point position specifying unit 112, a feature factor calculation unit 113, an index calculation unit 114, and a walking posture determination unit 115. And a display control unit 116.
 また、活動量計100の記憶部120は、加速度データ記憶部121と、特徴点位置記憶部122と、特徴因子記憶部123と、相関関係記憶部124と、指標記憶部125とを含む。 Further, the storage unit 120 of the activity meter 100 includes an acceleration data storage unit 121, a feature point position storage unit 122, a feature factor storage unit 123, a correlation storage unit 124, and an index storage unit 125.
 なお、本実施の形態においては、制御部110に含まれるこれらの各部は、制御部110によって、後述する図19の処理を実行するためのソフトウェアが実行されることによって、制御部110に構成されることとする。しかし、これに限定されず、制御部110に含まれるこれらの各部は、それぞれ、ハードウェア回路として制御部110の内部に構成されるようにしてもよい。 In the present embodiment, these units included in the control unit 110 are configured in the control unit 110 by executing software for executing the processing of FIG. 19 described later by the control unit 110. I will do it. However, the present invention is not limited to this, and each of these units included in the control unit 110 may be configured inside the control unit 110 as a hardware circuit.
 また、記憶部120に含まれるこれらの各部は、制御部110によって、後述する図19の処理を実行するためのソフトウェアが実行されることによって、記憶部120に一時的に構成されることとする。しかし、これに限定されず、記憶部120に含まれるこれらの各部は、それぞれ、専用の記憶装置として構成されるようにしてもよい。 Each of these units included in the storage unit 120 is temporarily configured in the storage unit 120 when the control unit 110 executes software for executing the processing of FIG. . However, the present invention is not limited to this, and each of these units included in the storage unit 120 may be configured as a dedicated storage device.
 また、記憶部120に含まれるこれらの各部は、記憶部120に構成されることに替えて、レジスタなどの制御部110の内蔵メモリに一時的に構成されるようにしてもよい。 Further, each of these units included in the storage unit 120 may be temporarily configured in a built-in memory of the control unit 110 such as a register instead of being configured in the storage unit 120.
 加速度読込制御部111は、加速度センサ170から3軸方向の加速度Ax(t),Ay(t),Az(t)を検出する。 The acceleration reading control unit 111 detects accelerations Ax (t), Ay (t), and Az (t) in three axes from the acceleration sensor 170.
 ここで、図6で説明したように、加速度センサの3軸方向がX軸、Y軸およびZ軸方向と一致している場合は、加速度センサで得られた検出値をそのままX軸、Y軸およびZ軸方向それぞれの加速度データAx(t),Ay(t),Az(t)とすればよい。 Here, as described with reference to FIG. 6, when the three-axis direction of the acceleration sensor coincides with the X-axis, Y-axis, and Z-axis directions, the detection values obtained by the acceleration sensor are directly used as the X-axis and Y-axis. And acceleration data Ax (t), Ay (t), Az (t) in the Z-axis direction.
 一方、加速度センサの3軸方向がX軸、Y軸およびZ軸方向と一致していない場合は、加速度センサで得られた検出値を座標変換することによって、X軸、Y軸およびZ軸方向それぞれの加速度データAx(t),Ay(t),Az(t)を算出する。 On the other hand, when the three-axis direction of the acceleration sensor does not coincide with the X-axis, Y-axis, and Z-axis directions, the X-axis, Y-axis, and Z-axis directions are converted by coordinate conversion of the detection values obtained by the acceleration sensor. The respective acceleration data Ax (t), Ay (t), Az (t) are calculated.
 そして、加速度読込制御部111は、サンプリング周期ごとの算出した加速度データAx(t),Ay(t),Az(t)を記憶部120の加速度データ記憶部121に記憶させる。 The acceleration reading control unit 111 stores the acceleration data Ax (t), Ay (t), and Az (t) calculated for each sampling period in the acceleration data storage unit 121 of the storage unit 120.
 特徴点位置特定部112は、加速度データ記憶部121に記憶された加速度データAx(t),Ay(t),Az(t)に基づいて、図6で説明したように、式1から式9までを用いて、X軸、Y軸およびZ軸方向それぞれの活動量計100の短時間(ここでは、±1歩分の時間(±T秒))での平均位置に対する相対位置X(t),Y(t),Z(t)を算出する。 The feature point position specifying unit 112 is based on the acceleration data Ax (t), Ay (t), Az (t) stored in the acceleration data storage unit 121, as described with reference to FIG. The relative position X (t) with respect to the average position in a short time (here, ± 1 step time (± T seconds)) of the activity meter 100 in the X-axis, Y-axis, and Z-axis directions , Y (t), Z (t) are calculated.
 次に、特徴点位置特定部112は、算出された位置X(t),Y(t),Z(t)に基づいて、図7から図9までで説明したような方法で、特徴点の位置の座標値を特定する。つまり、特徴点位置特定部112は、加速度センサ170によって検出された加速度に基づいて、Y軸方向(鉛直方向)、Z軸方向(進行方向)およびX軸方向(左右方向)の直交3軸方向のそれぞれに垂直な面であるXZ平面、XY平面およびYZ平面にZ軸方向の移動成分を除去して投影した軌跡の特徴点の位置を特定する。 Next, the feature point position specifying unit 112 uses the method described with reference to FIGS. 7 to 9 based on the calculated positions X (t), Y (t), and Z (t) to determine the feature points. Specify the coordinate value of the position. In other words, the feature point position specifying unit 112 is based on the acceleration detected by the acceleration sensor 170, and the orthogonal three-axis directions of the Y-axis direction (vertical direction), the Z-axis direction (traveling direction), and the X-axis direction (left-right direction). The position of the feature point of the trajectory projected by removing the moving component in the Z-axis direction on the XZ plane, the XY plane, and the YZ plane, which are planes perpendicular to each other, is specified.
 なお、特徴点は、すべて特定するのではなくて、後述する特徴因子の算出において必要なものだけを特定するようにしてもよい。 It should be noted that not all feature points are specified, but only those necessary for calculation of feature factors described later may be specified.
 そして、特徴点位置特定部112は、算出した特徴点の位置を特徴点位置記憶部122に記憶させる。 Then, the feature point position specifying unit 112 causes the feature point position storage unit 122 to store the calculated position of the feature point.
 特徴因子算出部113は、特徴点位置記憶部122に記憶された特徴点の位置に基づいて、図10から図12までで説明したような算出式に従って、特徴因子の値を算出する。そして、特徴因子算出部113は、算出した特徴因子の値を特徴因子記憶部123に記憶させる。 The feature factor calculation unit 113 calculates the value of the feature factor according to the calculation formulas described with reference to FIGS. 10 to 12 based on the position of the feature point stored in the feature point position storage unit 122. Then, the feature factor calculation unit 113 stores the calculated feature factor value in the feature factor storage unit 123.
 相関関係記憶部124には、前述した図13から図16で説明した重回帰式が予め記憶されている。 The correlation storage unit 124 stores in advance the multiple regression equations described above with reference to FIGS.
 指標算出部114は、相関関係記憶部124に記憶された重回帰式に従って、特徴因子記憶部123に記憶された特徴因子の値に基づいて、歩行姿勢を示す指標(たとえば、歩幅、歩隔、腰の回転、足上げ高さ、背筋の伸び、重心バランスなど)の値を算出する。そして、指標算出部114は、算出した指標の値を指標記憶部125に記憶させる。 The index calculation unit 114, based on the feature factor value stored in the feature factor storage unit 123, in accordance with the multiple regression equation stored in the correlation storage unit 124, indicates an index indicating the walking posture (for example, stride, step, Rotation of hips, leg height, back muscle stretch, center of gravity balance, etc.). Then, the index calculation unit 114 stores the calculated index value in the index storage unit 125.
 歩行姿勢判定部115は、指標記憶部125に記憶された指標の値に基づいて、歩行姿勢を判定する。 The walking posture determination unit 115 determines the walking posture based on the index value stored in the index storage unit 125.
 図19は、この実施の形態における歩行姿勢の判定の一例を示す第1の図である。図19を参照して、図19(A)で示すように歩幅が所定の閾値よりも広い方が、図19(B)で示すように歩幅が所定の閾値よりも狭い方よりも、歩行姿勢が良いと判定することが考えられる。 FIG. 19 is a first diagram showing an example of the determination of the walking posture in this embodiment. Referring to FIG. 19, as shown in FIG. 19 (A), when the stride is wider than the predetermined threshold, as shown in FIG. 19 (B), the walking posture is smaller than the stride whose width is smaller than the predetermined threshold. May be judged as good.
 また、図19(C)で示すように歩隔が所定の閾値よりも狭い方が、図19(D)で示すように歩隔が所定の閾値よりも広い方よりも、歩行姿勢が良いと判定することが考えられる。 In addition, as shown in FIG. 19C, the walking posture is better when the step is narrower than the predetermined threshold than when the step is wider than the predetermined threshold as shown in FIG. It is possible to judge.
 図20は、この実施の形態における歩行姿勢の判定の一例を示す第2の図である。ここで、歩幅をL、歩隔をW、腰の回転をD、足上げ高さをH、背筋の伸びをBとする。歩幅Lを分類するための閾値をa,b,c(a<b<c)とする。歩隔Wを分類するための閾値をd,e,f(d<e<f)とする。腰の回転Dを分類するための閾値をh,i,j(h<i<j)とする。足上げ高さHを分類するための閾値をk,l,m(k<l<m)とする。背筋の伸びBを分類するための閾値をn,o,p(n<o<p)とする。 FIG. 20 is a second diagram showing an example of the determination of the walking posture in this embodiment. Here, it is assumed that the step length is L, the step interval is W, the hip rotation is D, the foot-lifting height is H, and the back stretch is B. Threshold values for classifying the stride length L are a, b, c (a <b <c). It is assumed that thresholds for classifying the step W are d, e, f (d <e <f). The thresholds for classifying the hip rotation D are set as h, i, j (h <i <j). Threshold values for classifying the foot height H are k, l, and m (k <l <m). The thresholds for classifying the back stretch B are n, o, and p (n <o <p).
 図20を参照して、L<a,d≦W<e,i≦D<j,H<k,o≦B<pの各条件を満たす場合、歩行姿勢がAタイプであると判定し、a≦L<b,W<d,j≦D,k≦H<l,p≦Bの各条件を満たす場合、歩行姿勢がBタイプであると判定し、b≦L<c,e≦W<f,h≦D<i,l≦H<m,n≦B<oの各条件を満たす場合、歩行姿勢がCタイプであると判定し、c≦L,f≦W,D<h,m≦H,B<nの各条件を満たす場合、歩行姿勢がDタイプであると判定することが考えられる。 Referring to FIG. 20, when the conditions of L <a, d ≦ W <e, i ≦ D <j, H <k, o ≦ B <p are satisfied, it is determined that the walking posture is the A type, When the following conditions are satisfied: a ≦ L <b, W <d, j ≦ D, k ≦ H <l, p ≦ B, it is determined that the walking posture is the B type, and b ≦ L <c, e ≦ W <F, h ≦ D <i, l ≦ H <m, n ≦ B <o, the walking posture is determined to be the C type, and c ≦ L, f ≦ W, D <h, When the conditions of m ≦ H and B <n are satisfied, it may be determined that the walking posture is the D type.
 この実施の形態においては、歩行姿勢を示す指標を定量的に算出できるので、歩行姿勢の判定についても、様々な指標を組合せて、ユーザに応じたきめ細かい判定を行なうことが可能である。 In this embodiment, since the index indicating the walking posture can be calculated quantitatively, the determination of the walking posture can be performed in combination with various indexes to make a detailed determination according to the user.
 図18に戻って、表示制御部116は、歩行姿勢判定部115で判定された歩行姿勢の判定結果を表示部140に表示させるよう制御する。なお、活動量計100をパーソナルコンピュータなどの外部装置に接続して、外部装置の表示部に判定結果を表示させるようにしても良い。 18, the display control unit 116 controls the display unit 140 to display the determination result of the walking posture determined by the walking posture determination unit 115. The activity meter 100 may be connected to an external device such as a personal computer, and the determination result may be displayed on the display unit of the external device.
 図21は、この実施の形態における歩行姿勢の判定結果の表示の一例を示す図である。図21を参照して、右側の列の1番上に、目標が、「減量したい」こと、「体力を維持したい」こと、および、「若々しく歩きたい」ことであることが表示されている。この目標は、予めユーザによって、いくつかの目標候補の選択肢から選択されることによって設定される。 FIG. 21 is a diagram showing an example of the display of the walking posture determination result in this embodiment. Referring to FIG. 21, it is displayed at the top of the right column that the goal is “I want to lose weight”, “I want to maintain physical strength”, and “I want to walk younger”. Yes. This target is set in advance by the user selecting from several target candidate options.
 そして、その下に、目標の歩行姿勢を正面から見た画像および側面から見た画像が表示される。さらにその下に、算出した歩行姿勢を示す指標に基づいて生成されたユーザの歩行姿勢を正面から見た画像および側面から見た画像が表示される。また、ユーザの歩行姿勢の画像においては、歩行姿勢を示す指標のうち評価が低い指標に関連する体の部位に印(図では、楕円形の囲み)が表示される。 Below that, an image of the target walking posture viewed from the front and from the side is displayed. Below that, an image of the user's walking posture generated based on the calculated index indicating the walking posture as viewed from the front and an image as viewed from the side are displayed. Further, in the image of the user's walking posture, a mark (an oval box in the figure) is displayed on a body part related to an index having a low evaluation among the indexes indicating the walking posture.
 左側の列には、1番上に、歩行姿勢を示す指標のうち歩幅、歩隔および腰の回転についてのレーダーチャートおよび重心バランスが左右のどの辺りにあるかを示すチャートが表示される。これらのチャートにおいては、目標の歩行姿勢の指標の値が、ひし形のプロットで示され、ユーザの歩行姿勢の指標の値が、正方形のプロットで示される。 In the left column, a radar chart for stride, step and hip rotation, and a chart showing where the balance of the center of gravity is located on the left and right of the index indicating the walking posture are displayed at the top. In these charts, the target walking posture index value is indicated by a rhombus plot, and the user walking posture index value is indicated by a square plot.
 レーダーチャートでは、歩幅、歩隔および腰の回転について、目標の値が、4段階のうち、それぞれ、3段階目であるのに対して、ユーザの値が、それぞれ、2段階目、3段階目、1段階目であることが示されている。また、重心バランスのチャートにおいては、目標のバランスが、中央であることに対して、ユーザのバランスは、右寄りであることが示されている。このように腰の回転が目標に対して比較的低くなっているので、前述したように、右側の列のユーザの歩行姿勢の画像で、腰の位置に印が付されている。 In the radar chart, the target value for the stride, step, and hip rotation is the third stage of each of the four stages, whereas the user value is the second stage and the third stage, respectively. The first stage is shown. Further, the center of gravity balance chart shows that the balance of the user is to the right while the balance of the target is the center. As described above, since the hip rotation is relatively low with respect to the target, as described above, the waist position is marked in the image of the walking posture of the user in the right column.
 なお、印を付すことに替えて、色を変えるなどにより、注意する点を表示するようにしてもよい。たとえば、歩幅にギャップがある場合は、足の色を赤くする。 In addition, it may be made to display the point to be careful by changing a color instead of attaching a mark. For example, if there is a gap in the stride, the foot color will be red.
 左側の列のチャートの下には、ユーザへのアドバイスが表示される。ここでは、歩幅の値が目標より少し低く、腰の回転が目標より比較的低くなっているので、「歩幅を広く、腰を回転させて歩いてください」といったアドバイスが表示されている。このようなアドバイスは、目標との乖離の度合に応じて、予め活動量計100の記憶部120に記憶されており、歩行姿勢判定部115によって、記憶されているアドバイスのうちから、目標との乖離の度合に応じたアドバイスが選択され、選択されたアドバイスが、表示制御部116によって表示部140に表示される。 The advice to the user is displayed under the chart in the left column. Here, since the value of the stride is slightly lower than the target and the hip rotation is relatively lower than the target, advice such as “Wide the stride, rotate the hips and walk” is displayed. Such advice is stored in advance in the storage unit 120 of the activity meter 100 in accordance with the degree of deviation from the target, and the walking posture determination unit 115 determines the The advice corresponding to the degree of deviation is selected, and the selected advice is displayed on the display unit 140 by the display control unit 116.
 なお、アドバイスに替えて、または、アドバイスとともに、目標の歩行姿勢に近づくためのトレーニング内容を表示するようにしてもよい。 It should be noted that the training content for approaching the target walking posture may be displayed instead of or together with the advice.
 図22は、この実施の形態におけるユーザの歩行姿勢の画像の表示の一例を示す図である。図22を参照して、図22(A)は、標準的な歩行姿勢を示す画像である。図22(B)は、歩幅が大きい場合の歩行姿勢を示す画像である。図22(C)は、重心バランスが右側によっている場合の歩行姿勢を示す画像である。 FIG. 22 is a diagram showing an example of the display of the user's walking posture image in this embodiment. Referring to FIG. 22, FIG. 22 (A) is an image showing a standard walking posture. FIG. 22B is an image showing a walking posture when the stride is large. FIG. 22C is an image showing a walking posture when the center of gravity balance is on the right side.
 図23は、この実施の形態における活動量計100の制御部110によって実行される歩行姿勢判定処理の流れを示すフローチャートである。図23を参照して、ステップS101で、制御部110は、加速度センサ170から加速度センサの検出値を読込み、図18の加速度読込制御部111で説明したように、加速度データAx(t),Ay(t),Az(t)を、サンプリング周期ごとに、記憶部120に記憶させる。 FIG. 23 is a flowchart showing the flow of the walking posture determination process executed by the control unit 110 of the activity meter 100 in this embodiment. Referring to FIG. 23, in step S101, control unit 110 reads the detected value of the acceleration sensor from acceleration sensor 170, and as described in acceleration reading control unit 111 in FIG. 18, acceleration data Ax (t), Ay (T) and Az (t) are stored in the storage unit 120 for each sampling period.
 次に、ステップS102で、制御部110は、1歩分の歩行を検出したか否かを判断する。ここでは、図7で説明した特徴点(1)(特徴点(5))が検出されることによって、一歩分が検出されたと判断する。一歩分を検出してないと判断した場合(ステップS102でNOと判断した場合)、制御部110は、実行する処理をステップS111の処理に進める。 Next, in step S102, the control unit 110 determines whether or not one step of walking has been detected. Here, it is determined that one step has been detected by detecting the feature point (1) (feature point (5)) described in FIG. If it is determined that one step has not been detected (NO in step S102), control unit 110 advances the process to be executed to step S111.
 一方、一歩分を検出したと判断した場合(ステップS102でYESと判断した場合)、ステップS103で、制御部110は、ステップS101で記憶部120に記憶された1歩分の加速度データAx(t),Ay(t),Az(t)を読出し、図18の特徴点位置特定部112で説明したように、特徴点の位置の座標値を算出する。 On the other hand, if it is determined that one step has been detected (YES in step S102), in step S103, the control unit 110 stores acceleration data Ax (t for one step stored in the storage unit 120 in step S101. ), Ay (t), Az (t) are read, and the coordinate value of the position of the feature point is calculated as described in the feature point position specifying unit 112 in FIG.
 次に、ステップS104で、制御部110は、ステップS103で算出された特徴点の位置の座標値に基づいて、図18の特徴因子算出部113で説明したように、特徴因子の値を算出する。 Next, in step S104, the control unit 110 calculates the value of the feature factor based on the coordinate value of the position of the feature point calculated in step S103, as described in the feature factor calculation unit 113 in FIG. .
 次いで、ステップS105で、制御部110は、ステップS104で算出された特徴因子の値に基づいて、図18の指標算出部114で説明したように、特徴因子と歩行姿勢の指標との相関関係に従い、指標の値を算出して、記憶部120に記憶させる。その後、制御部110は、実行する処理をステップS11の処理に進める。 Next, in step S105, the control unit 110 follows the correlation between the feature factor and the walking posture index as described in the index calculation unit 114 of FIG. 18 based on the value of the feature factor calculated in step S104. The index value is calculated and stored in the storage unit 120. Then, the control part 110 advances the process to perform to the process of step S11.
 ステップS111では、制御部110は、操作部130がユーザによって操作されることによって、歩行姿勢の判定結果を表示する指示が受付けられたか否かを判断する。結果表示指示が受付けられていないと判断した場合(ステップS111でNOと判断した場合)、制御部110は、実行する処理をステップS101の処理に戻す。 In step S111, the control unit 110 determines whether or not an instruction to display the determination result of the walking posture is received by the operation unit 130 being operated by the user. If it is determined that the result display instruction has not been received (NO in step S111), control unit 110 returns the process to be executed to the process in step S101.
 一方、結果表示指示が受付けられたと判断した場合(ステップS111でYESと判断した場合)、ステップS112で、制御部110は、ステップS105で記憶部120に記憶された歩行姿勢を示す指標を読出し、当該指標に基づいて、図18の歩行姿勢判定部115で説明したように、歩行姿勢を判定する。 On the other hand, when it is determined that the result display instruction is accepted (when YES is determined in step S111), in step S112, the control unit 110 reads the index indicating the walking posture stored in the storage unit 120 in step S105, Based on the index, the walking posture is determined as described in the walking posture determination unit 115 of FIG.
 次に、ステップS113で、制御部110は、図18の表示制御部116で説明したように、ステップS112で判定された歩行姿勢の判定結果を表示部140に表示させるよう制御する。その後、制御部110は、実行する処理をステップS101の処理に戻す。 Next, in step S113, the control unit 110 controls the display unit 140 to display the determination result of the walking posture determined in step S112, as described in the display control unit 116 of FIG. Then, the control part 110 returns the process to perform to the process of step S101.
 (1) 以上説明したように、本実施の形態における活動量計100は、本体部191と、加速度センサ170と、制御部110とを備え、本体部191を腰に装着するユーザの歩行姿勢を判定するための装置である。本体部191が装着される腰の歩行時の進行方向(Z軸方向)の移動成分を除去した3次元の軌跡は、図7から図9までで説明したパターンを有する。当該パターンは、当該パターンの特徴を規定する特徴点を複数含む。 (1) As described above, the activity meter 100 according to the present embodiment includes the main body 191, the acceleration sensor 170, and the control unit 110, and the walking posture of the user who wears the main body 191 on the waist. It is an apparatus for determining. The three-dimensional trajectory from which the moving component in the advancing direction (Z-axis direction) during walking of the waist to which the main body 191 is attached has the pattern described with reference to FIGS. The pattern includes a plurality of feature points that define the features of the pattern.
 制御部110は、加速度センサ170によって検出された加速度に基づいて、鉛直方向(Y軸方向)、進行方向(Z軸方向)および左右方向(X軸方向)のそれぞれに垂直な面であるXZ平面、XY平面およびYZ平面に、進行方向(Z軸方向)の移動成分を除去して投影した軌跡の特徴点の位置を特定する特徴点位置特定部112と、特徴点位置特定部112によって特定された位置に基づいて、軌跡の特徴因子の値を算出する特徴因子算出部113と、特徴因子の値と歩行姿勢を示す指標の値との予め求められた相関関係に従って、特徴因子算出部113によって算出された特徴因子の値に基づいて、指標の値を算出する指標算出部114と、指標算出部114によって算出された指標の値に基づいて、歩行姿勢を判定する歩行姿勢判定部115とを含む。 Based on the acceleration detected by the acceleration sensor 170, the control unit 110 is an XZ plane that is a plane perpendicular to the vertical direction (Y-axis direction), the traveling direction (Z-axis direction), and the left-right direction (X-axis direction). The feature point position specifying unit 112 that specifies the position of the feature point of the locus projected by removing the moving component in the traveling direction (Z-axis direction) on the XY plane and the YZ plane, and the feature point position specifying unit 112 The feature factor calculation unit 113 that calculates the value of the feature factor of the trajectory based on the determined position, and the feature factor calculation unit 113 according to the correlation obtained in advance between the value of the feature factor and the value of the index indicating the walking posture. An index calculation unit 114 that calculates an index value based on the calculated feature factor value, and a walking posture determination that determines a walking posture based on the index value calculated by the index calculation unit 114 And a part 115.
 このため、精度よい相関関係に基づいて、歩行姿勢を示す指標が精度良く算出され、また、様々な歩行姿勢を示す指標が算出されるので、より精度良く詳細な歩行姿勢を評価することができる。 For this reason, since the index indicating the walking posture is accurately calculated based on the accurate correlation, and the index indicating the various walking postures is calculated, the detailed walking posture can be evaluated more accurately. .
 (2) また、活動量計100は、さらに、表示部140を備える。制御部110は、歩行姿勢判定部115によって判定された歩行姿勢と目標の歩行姿勢とを比較可能に表示部140に表示させる表示制御部116を含む。このため、ユーザの歩行姿勢の状況を判り易く表示することができる。 (2) The activity meter 100 further includes a display unit 140. The control unit 110 includes a display control unit 116 that causes the display unit 140 to display the walking posture determined by the walking posture determination unit 115 and the target walking posture in a comparable manner. For this reason, the user's walking posture can be easily displayed.
 (3) また、制御部110は、歩行姿勢判定部115によって判定された歩行姿勢を改善するためのアドバイスを表示部140に表示させる表示制御部116を含む。このため、ユーザの歩行姿勢の状況を判り易く表示することができる。 (3) Further, the control unit 110 includes a display control unit 116 that displays on the display unit 140 advice for improving the walking posture determined by the walking posture determination unit 115. For this reason, the user's walking posture can be easily displayed.
 (4) また、図13から図16で説明したように、相関関係は、重回帰分析によって得られる、目標変数としての特徴因子の値と説明変数としての指標の値との関係式である重回帰式で示される。 (4) Also, as described with reference to FIGS. 13 to 16, the correlation is a relational expression obtained by multiple regression analysis, which is a relational expression between a characteristic factor value as a target variable and an index value as an explanatory variable. It is shown by a regression equation.
 (5) さらに、特徴点は、右足が接地したときの特徴点(1)、および、右足で立脚している間で軌跡が最も高い位置に達したときの特徴点(2)、ならびに、左足が接地したときの特徴点(3)、および左足で立脚している間で軌跡が最も高い位置に達したときの特徴点(4)を含む。 (5) Furthermore, the feature points are the feature point (1) when the right foot touches down, the feature point (2) when the locus reaches the highest position while standing on the right foot, and the left foot This includes a feature point (3) when the robot touches down and a feature point (4) when the locus reaches the highest position while standing on the left foot.
 特徴因子は、進行方向(Z軸方向)に垂直なXY平面に投影した軌跡における特徴点(1)と特徴点(2)との鉛直方向(Y軸方向)の距離である特徴因子Hr、および、左右方向(X軸方向)に垂直なYZ平面に投影した軌跡における特徴点(1)と特徴点(2)との距離および特徴点(3)と特徴点(4)との距離から算出される特徴因子StShiを含む。 The feature factor is a feature factor Hr that is the distance in the vertical direction (Y-axis direction) between the feature point (1) and the feature point (2) in the locus projected on the XY plane perpendicular to the traveling direction (Z-axis direction), and , Calculated from the distance between the feature point (1) and the feature point (2) and the distance between the feature point (3) and the feature point (4) in the locus projected on the YZ plane perpendicular to the left-right direction (X-axis direction). The characteristic factor StShi is included.
 指標は、歩幅を含む。重回帰式は、重回帰分析によって得られた偏回帰係数αおよび特徴因子Hrの積と、重回帰分析によって得られた偏回帰係数βおよび特徴因子StShiの積と、偏回帰係数γとの和を算出する歩幅Length=α×Hr+β×StShi+γの式である。 * The index includes stride. The multiple regression equation is the sum of the product of the partial regression coefficient α and the characteristic factor Hr obtained by the multiple regression analysis, the product of the partial regression coefficient β and the feature factor StShi obtained by the multiple regression analysis, and the partial regression coefficient γ. Is a step length = α × Hr + β × StShi + γ.
 (6) さらに、特徴点は、右足が接地したときの特徴点(1)、右足で立脚している間で軌跡が最も高い位置に達したときの特徴点(2)、軌跡の最も右側の特徴点(6)、および、軌跡の最も左側の特徴点(7)、ならびに、軌跡の右側で最も前側の特徴点(10)、軌跡の左側で最も前側の特徴点(12)、軌跡の右側で最も後ろ側の特徴点(9)、および、軌跡の左側で最も後ろ側の特徴点(11)を含む。 (6) Furthermore, the feature points are the feature point (1) when the right foot touches down, the feature point (2) when the trajectory reaches the highest position while standing on the right foot, and the rightmost point of the trajectory. The feature point (6), the leftmost feature point (7) of the locus, the foremost feature point (10) on the right side of the locus, the foremost feature point (12) on the left side of the locus, and the right side of the locus And the rearmost feature point (9) and the rearmost feature point (11) on the left side of the locus.
 特徴因子は、進行方向(Z軸方向)に垂直なXY平面に投影した軌跡における特徴点(1)と特徴点(2)との鉛直方向(Y軸方向)の距離Hrを特徴点(6)と特徴点(7)との左右方向(X軸方向)の距離Wで割った商である特徴因子Hr/W、および、鉛直方向(Y軸方向)に垂直なXZ平面に投影した軌跡における特徴点(10)と特徴点(12)との左右方向(X軸方向)の距離WuSuを特徴点(9)と特徴点(11)との左右方向(X軸方向)の距離WdSuで割った商である特徴因子WuSu/WdSuを含む。 The feature factor is a distance Hr in the vertical direction (Y-axis direction) between the feature point (1) and the feature point (2) in the locus projected on the XY plane perpendicular to the traveling direction (Z-axis direction). And a feature factor Hr / W, which is a quotient divided by a distance W in the left-right direction (X-axis direction) from the feature point (7), and a feature in the locus projected on the XZ plane perpendicular to the vertical direction (Y-axis direction) The quotient obtained by dividing the distance WuSu between the point (10) and the feature point (12) in the left-right direction (X-axis direction) by the distance WdSu between the feature point (9) and the feature point (11) in the left-right direction (X-axis direction). The characteristic factor WuSu / WdSu is included.
 指標は、歩隔を含む。重回帰式は、重回帰分析によって得られた偏回帰係数δおよび特徴因子Hr/Wの積と、重回帰分析によって得られた偏回帰係数εおよび特徴因子WuSu/WdSuの積と、偏回帰係数ζとの和を算出する歩隔Width=δ×Hr/W+ε×WuSu/WdSu+ζの式である。 * The index includes the step interval. The multiple regression equation is a product of the partial regression coefficient δ and the characteristic factor Hr / W obtained by the multiple regression analysis, a product of the partial regression coefficient ε and the feature factor WuSu / WdSu obtained by the multiple regression analysis, and the partial regression coefficient. This is the formula for calculating the sum with ζ: Width = δ × Hr / W + ε × WuSu / WdSu + ζ.
 次に、上述した実施の形態の変形例について説明する。
 (1) 前述した実施の形態においては、指標の値と閾値との関係に基づいて、歩行姿勢を判定するようにした。しかし、これに限定されず、歩行姿勢との関係が予め求められた指標の組合せと、算出された指標の組合せとの類似度に基づいて、歩行姿勢を判定するようにしてもよい。
Next, a modification of the above-described embodiment will be described.
(1) In the embodiment described above, the walking posture is determined based on the relationship between the index value and the threshold value. However, the present invention is not limited to this, and the walking posture may be determined on the basis of the similarity between the combination of the index whose relationship with the walking posture is obtained in advance and the calculated combination of the index.
 (2) 歩行姿勢を示す指標の閾値は、実際に歩行姿勢の良い人に歩いてもらったときの実測データに基づいて、定めるようにしてもよい。 (2) The threshold value of the index indicating the walking posture may be determined based on actually measured data when a person with a good walking posture actually walks.
 (3) 前述した実施の形態においては、図21で説明したように、目標の歩行姿勢とユーザの歩行姿勢とを別々に表示するようにした。しかし、これに限定されず、目標の歩行姿勢とユーザの歩行姿勢とを重ねて表示するようにしてもよい。 (3) In the embodiment described above, the target walking posture and the user's walking posture are displayed separately as described in FIG. However, the present invention is not limited to this, and the target walking posture and the user's walking posture may be displayed in an overlapping manner.
 (4) 前述した実施の形態においては、式4から式6において説明したように、平均速度成分は、±1歩分の時間の平均速度成分であることとした。しかし、これに限定されず、±n歩(nは所定数)分の時間の平均速度成分であることとしてもよいし、-n歩(算出対象の時間の前、n歩)分の時間の平均速度成分であることとしてもよいし、±s秒(sは所定数)の平均速度成分としてもよいし、-s秒(算出対象の前、s秒)の平均速度成分としてもよい。 (4) In the above-described embodiment, as described in Expressions 4 to 6, the average speed component is an average speed component for the time of ± 1 step. However, the present invention is not limited to this, and it may be an average speed component for a time of ± n steps (n is a predetermined number) or a time of −n steps (n steps before the calculation target time). It may be an average velocity component, may be an average velocity component of ± s seconds (s is a predetermined number), or may be an average velocity component of -s seconds (before calculation target, s seconds).
 (5) 前述した実施の形態においては、活動量計100の装置の発明として説明した。しかし、これに限定されず、活動量計100を制御するための制御方法の発明として捉えることができる。 (5) In the above-described embodiment, the invention of the device for the activity meter 100 has been described. However, the present invention is not limited to this, and can be understood as an invention of a control method for controlling the activity meter 100.
 (6) 今回開示された実施の形態はすべての点で例示であって制限的なものではないと考えられるべきである。本発明の範囲は、上記した説明ではなく、請求の範囲によって示され、請求の範囲と均等の意味および範囲内でのすべての変更が含まれることが意図される。 (6) The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.
 100 活動量計、110 制御部、111 加速度読込制御部、112 特徴点位置特定部、113 特徴因子算出部、114 指標算出部、115 歩行姿勢判定部、116 表示制御部、120 メモリ、121 加速度データ記憶部、122 特徴点位置記憶部、123 特徴因子記憶部、124 相関関係記憶部、125 指標記憶部、130 操作部、131 表示切換/決定スイッチ、132 左操作/メモリスイッチ、133 右操作スイッチ、140 表示部、141 ディスプレイ、170 加速度センサ、190 電源、191 本体部、192 クリップ部。 100 activity meter, 110 control unit, 111 acceleration reading control unit, 112 feature point position specifying unit, 113 feature factor calculation unit, 114 index calculation unit, 115 walking posture determination unit, 116 display control unit, 120 memory, 121 acceleration data Storage unit 122 Feature point location storage unit 123 Feature factor storage unit 124 Correlation storage unit 125 Index storage unit 130 Operation unit 131 Display change / decision switch 132 Left operation / memory switch 133 Right operation switch 140 display part, 141 display, 170 acceleration sensor, 190 power supply, 191 main body part, 192 clip part.

Claims (6)

  1.  本体部(191)と、前記本体部の加速度を検出するための加速度センサ(170)と、制御部(110)とを備え、前記本体部を所定部位に装着するユーザの歩行姿勢を判定するための歩行姿勢判定装置(100)であって、
     前記本体部が装着される前記所定部位の歩行時の進行方向の移動成分を除去した3次元の軌跡は、パターンを有し、当該パターンは、当該パターンの特徴を規定する特徴点を複数含み、
     前記制御部は、
      前記加速度センサによって検出された加速度に基づいて、鉛直方向、前記進行方向および左右方向の直交3軸方向のそれぞれに垂直な面に前記進行方向の移動成分を除去して投影した前記軌跡の前記特徴点の位置を特定する特定手段(112)と、
      前記特定手段によって特定された前記位置に基づいて、前記軌跡の特徴因子の値を算出する第1の算出手段(113)と、
      前記特徴因子の値と前記歩行姿勢を示す指標の値との予め求められた相関関係に従って、前記第1の算出手段によって算出された前記特徴因子の値に基づいて、前記指標の値を算出する第2の算出手段(114)と、
      前記第2の算出手段によって算出された前記指標の値に基づいて、前記歩行姿勢を判定する判定手段(115)とを含む、歩行姿勢判定装置。
    A body part (191), an acceleration sensor (170) for detecting acceleration of the body part, and a control part (110) are provided to determine the walking posture of a user who wears the body part at a predetermined site. The walking posture determination device (100) of
    The three-dimensional trajectory from which the moving component in the advancing direction during walking of the predetermined part to which the main body part is attached has a pattern, and the pattern includes a plurality of feature points that define the characteristics of the pattern,
    The controller is
    The feature of the locus projected by removing the moving component in the traveling direction on a plane perpendicular to each of the three orthogonal directions of the vertical direction, the traveling direction, and the left-right direction based on the acceleration detected by the acceleration sensor. A specifying means (112) for specifying the position of the point;
    First calculating means (113) for calculating a value of a characteristic factor of the trajectory based on the position specified by the specifying means;
    The value of the index is calculated based on the value of the characteristic factor calculated by the first calculation means according to a correlation previously obtained between the value of the characteristic factor and the value of the index indicating the walking posture. Second calculating means (114);
    A walking posture determination apparatus, comprising: a determination unit (115) for determining the walking posture based on the value of the index calculated by the second calculation unit.
  2.  前記歩行姿勢判定装置は、さらに、表示部(140)を備え、
     前記制御部は、さらに、
      前記判定手段によって判定された前記歩行姿勢と目標の歩行姿勢とを比較可能に前記表示部に表示させる表示制御手段(116)を含む、請求項1に記載の歩行姿勢判定装置。
    The walking posture determination device further includes a display unit (140),
    The control unit further includes:
    The walking posture determination device according to claim 1, further comprising display control means (116) for causing the display unit to display the walking posture determined by the determination unit and the target walking posture in a comparable manner.
  3.  前記歩行姿勢判定装置は、さらに、表示部(140)を備え、
     前記制御部は、さらに、
      前記判定手段によって判定された前記歩行姿勢を改善するためのアドバイスを前記表示部に表示させる表示制御手段(116)を含む、請求項1に記載の歩行姿勢判定装置。
    The walking posture determination device further includes a display unit (140),
    The control unit further includes:
    The walking posture determination device according to claim 1, further comprising display control means (116) for displaying advice for improving the walking posture determined by the determination means on the display unit.
  4.  前記相関関係は、重回帰分析によって得られる、目的変数としての前記特徴因子の値と説明変数としての前記指標の値との関係式である重回帰式で示される、請求項1に記載の歩行姿勢判定装置。 The walking according to claim 1, wherein the correlation is represented by a multiple regression equation that is a relational expression between the value of the characteristic factor as an objective variable and the value of the index as an explanatory variable, obtained by multiple regression analysis. Posture determination device.
  5.  前記特徴点は、第1の足が接地したときの第1の特徴点、および、前記第1の足で立脚している間で前記軌跡が最も高い位置に達したときの第2の特徴点、ならびに、前記第2の足が接地したときの第3の特徴点、および、前記第2の足で立脚している間で前記軌跡が最も高い位置に達したときの第4の特徴点を含み、
     前記特徴因子は、前記進行方向に垂直な面に投影した前記軌跡における前記第1の特徴点と前記第2の特徴点との前記鉛直方向の距離である第1の特徴因子、および、前記左右方向に垂直な面に投影した前記軌跡における前記第1の特徴点と前記第2の特徴点との距離および前記第3の特徴点と前記第4の特徴点との距離から算出される第2の特徴因子を含み、
     前記指標は、歩幅を含み、
     前記重回帰式は、前記重回帰分析によって得られた第1の偏回帰係数および前記第1の特徴因子の積と、前記重回帰分析によって得られた第2の偏回帰係数および前記第2の特徴因子の積と、第3の偏回帰係数との和を算出する式である、請求項4に記載の歩行姿勢判定装置。
    The feature points include a first feature point when the first foot is grounded and a second feature point when the locus reaches the highest position while standing on the first foot. And a third feature point when the second foot is grounded, and a fourth feature point when the locus reaches the highest position while standing on the second foot. Including
    The feature factor is a first feature factor that is a distance in the vertical direction between the first feature point and the second feature point in the locus projected on a plane perpendicular to the traveling direction, and the left and right A second calculated from a distance between the first feature point and the second feature point and a distance between the third feature point and the fourth feature point in the locus projected on a plane perpendicular to the direction; Including the characteristic factors of
    The indicator includes a stride,
    The multiple regression equation includes a product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, a second partial regression coefficient obtained by the multiple regression analysis, and the second The walking posture determination apparatus according to claim 4, wherein the walking posture determination apparatus is an expression for calculating a sum of a product of characteristic factors and a third partial regression coefficient.
  6.  前記特徴点は、第1の足が接地したときの第1の特徴点、前記第1の足で立脚している間で前記軌跡が最も高い位置に達したときの第2の特徴点、前記軌跡の最も右側の第3の特徴点、および、前記軌跡の最も左側の第4の特徴点、ならびに、前記軌跡の右側で最も前側の第5の特徴点、前記軌跡の左側で最も前側の第6の特徴点、前記軌跡の右側で最も後ろ側の第7の特徴点、および、前記軌跡の左側で最も後ろ側の第8の特徴点を含み、
     前記特徴因子は、前記進行方向に垂直な面に投影した前記軌跡における前記第1の特徴点と前記第2の特徴点との前記鉛直方向の距離を前記第3の特徴点と前記第4の特徴点との前記左右方向の距離で割った商である第1の特徴因子、および、前記鉛直方向に垂直な面に投影した前記軌跡における前記第5の特徴点と前記第6の特徴点との前記左右方向の距離を前記第7の特徴点と前記第8の特徴点との前記左右方向の距離で割った商である第2の特徴因子を含み、
     前記指標は、歩隔を含み、
     前記重回帰式は、前記重回帰分析によって得られた第1の偏回帰係数および前記第1の特徴因子の積と、前記重回帰分析によって得られた第2の偏回帰係数および前記第2の特徴因子の積と、第3の偏回帰係数との和を算出する式である、請求項4に記載の歩行姿勢判定装置。
    The feature point is a first feature point when the first foot is grounded, a second feature point when the locus reaches the highest position while standing on the first foot, A third feature point on the rightmost side of the locus, a fourth feature point on the leftmost side of the locus, a fifth feature point on the rightmost side on the right side of the locus, and a frontmost fifth feature point on the left side of the locus. 6 feature points, a seventh feature point that is the rearmost on the right side of the trajectory, and an eighth feature point that is the rearmost on the left side of the trajectory,
    The feature factor is a distance in the vertical direction between the first feature point and the second feature point in the locus projected on a plane perpendicular to the traveling direction, and the third feature point and the fourth feature point. A first feature factor that is a quotient divided by the distance in the left-right direction with respect to the feature point, and the fifth feature point and the sixth feature point in the locus projected on a plane perpendicular to the vertical direction A second feature factor that is a quotient obtained by dividing the distance in the left-right direction by the distance in the left-right direction between the seventh feature point and the eighth feature point;
    The indicator includes a step,
    The multiple regression equation includes a product of the first partial regression coefficient and the first characteristic factor obtained by the multiple regression analysis, a second partial regression coefficient obtained by the multiple regression analysis, and the second The walking posture determination apparatus according to claim 4, wherein the walking posture determination apparatus is an expression for calculating a sum of a product of characteristic factors and a third partial regression coefficient.
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