JP2006175206A - Apparatus, method and program for detecting physical condition - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a physical condition detecting device capable of precisely detecting a physical condition irrespective of the direction of an acceleration sensor mounted thereon, its detecting method, and its detecting program. <P>SOLUTION: This physical condition detecting apparatus is equipped with a three-axis acceleration sensor (1) to be mounted on a human body, a data collecting means (2) for collecting acceleration vector data outputted by the three-axis acceleration sensor (1) at prescribed sampling intervals, and a processing means (5). It is characterized by the processing means (5) which detects the body's condition in walking by using the acceleration vector data collected successively, and has a function of automatically correcting the gravity acceleration direction axis and the body axis in the coordination system of the acceleration sensor by using the successive acceleration vector data within a certain period of time in walking. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a body state detection apparatus, a detection method, and a detection program for detecting a walking state or posture of a person in daily life or work using a triaxial acceleration sensor.

  Conventionally, various methods have been proposed in which an acceleration sensor is attached to the body, a change in the gravitational acceleration direction is obtained from a change in the output signal of the acceleration sensor due to a change in the posture of the body, and a body posture is detected. For example, the following Non-Patent Document 1 proposes a method of detecting walking, stair climbing and the like by a combination of an acceleration sensor and an angular velocity sensor.

Further, in Patent Document 1 below, by measuring the direct current component of the output signal of the acceleration sensor while a person is not operating, the inclination angle of the acceleration sensor (specifically, a pacemaker with a built-in acceleration sensor) is measured. A body motion detecting device capable of obtaining the angle of elevation and the angle of deviation is disclosed.
Japanese Patent Laid-Open No. 11-42220 Koki and Kurata, Personal Positioning Method Based on Walking Motion Analysis Using Inertial Sensors and Wearable Cameras, IEICE Technical Report, PRMU2003-260, pp.25-30, 2004

  However, in the conventional method, accurate measurement requires that the acceleration sensor is accurately attached to the direction of gravitational acceleration when attached to the body, but this is not easy.

  In Patent Document 1, the initial inclination angle of the acceleration sensor can be obtained, but a complicated process of measuring the direct current component of the output signal of the acceleration sensor in a non-operating state is necessary.

  Furthermore, even if the acceleration sensor can be correctly attached, if the attachment direction of the acceleration sensor may change from the initial direction due to a change in the body condition after attachment, the gravitational acceleration direction is set in advance. In the method of setting, there is a problem that the body state cannot be detected correctly.

  SUMMARY OF THE INVENTION An object of the present invention is to solve the above-mentioned problems, and does not depend on the mounting direction of the acceleration sensor, and can detect a body state such as a walking state and a posture inclination with high accuracy, and a detection method thereof. And providing a detection program.

  The object of the present invention is achieved by the following means.

  That is, a body state detection device (1) according to the present invention includes a triaxial acceleration sensor worn on the body, and data collection means for collecting acceleration vector data output from the triaxial acceleration sensor at a predetermined sampling interval. And a processing means, wherein the processing means detects the walking state of the body using the acceleration vector data continuously collected, and uses the continuous acceleration vector data within the period of the walking state. It is characterized by determining a gravitational acceleration vector or a body axis.

Moreover, the physical condition detection apparatus (2) according to the present invention is the above-described physical condition detection apparatus (1), in which the processing means performs absolute values of the respective acceleration vectors with respect to the acceleration vector data collected continuously. The average value of these absolute values and the standard deviation are calculated, and the standard deviation, the number of times the absolute value exceeds the average value, and the sum of the time when the absolute value exceeds the average value are used. When a walking state is detected and the walking period determined to be the walking state continues, the walking time is summed to calculate a continuous time, and when the continuous time exceeds a predetermined time, at least the latest An average vector of acceleration vector data collected during the walking period is calculated, and the average vector is determined as a gravitational acceleration vector.

  Further, in the body state detection device (3) according to the present invention, in the body state detection device (2), the processing means determines the gravity acceleration vector next with reference to the direction of the gravity acceleration vector. Up to this point, the angle of each acceleration vector data is calculated, and the angle is determined as the inclination angle of the body wearing the acceleration sensor.

  In addition, the physical condition detection device (4) according to the present invention is the above-described physical condition detection device (1), wherein the processing means performs absolute value of each acceleration vector with respect to the acceleration vector data collected continuously. The average value, standard deviation, and periodicity of these absolute values are calculated, and when the standard deviation is within a predetermined range and the periodicity is greater than the predetermined value, the walking state is determined. It is said.

  Further, the body condition detection device (5) according to the present invention is the principal component analysis for the acceleration vector data in the period in which the processing means is determined to be the walking state in the body state detection device (4). The first principal component, the second principal component, and the third principal component are used as the body axis.

  Further, in the body state detection device (6) according to the present invention, in the body state detection device (5), the processing means obtains an average acceleration vector from the acceleration vector data as a target of the principal component analysis, The direction of the first principal component close to the average acceleration vector is determined as a direction directly below the body, and the forward direction of the body is determined according to the time change of the component of the second principal component direction of the acceleration vector data. The direction of the third principal component orthogonal to the right downward direction and the forward direction in the right-handed system is determined as the right lateral direction of the body.

  Further, the physical condition detection device (7) according to the present invention is the physical condition detection device (6) described above, wherein the processing means positively determines one direction of the second principal component in determining the body advance direction. Assuming that the time component of the second principal component direction of the acceleration vector data is similar to the whole or a part of a sawtooth wave having a slowly decreasing portion, the second principal component When one direction is determined as a forward direction of the body, and the time change of the component in the second principal component direction of the acceleration vector data is similar to all or part of a sawtooth wave having a slowly increasing portion, A direction opposite to the one direction of the second main component is determined as a body advance direction.

  Further, in the physical condition detection device (8) according to the present invention, in the physical condition detection device (6) or (7), the processing means obtains an average acceleration vector of the acceleration vector data, and the average acceleration vector And an angle formed with each of the directly downward direction, the forward direction, and the right lateral direction is obtained as an inclination angle of the body wearing the acceleration sensor.

The body condition detection method (1) according to the present invention includes a first step of collecting acceleration vector data at a predetermined sampling interval using a three-axis acceleration sensor attached to the body, A second step of detecting a walking state of the body using acceleration vector data; and a third step of determining a gravitational acceleration vector or a body axis using continuous acceleration vector data within a period of the walking state. It is characterized by that.

  Also, the body state detection method (2) according to the present invention is the body state detection method (1) described above, wherein the second step is the absolute value of each acceleration vector with respect to the acceleration vector data collected continuously. The fourth step of calculating the value, the average value of these absolute values, and the standard deviation, the standard deviation, the number of times the absolute value exceeds the average value, and the time during which the absolute value exceeds the average value. A fifth step of detecting the walking state using a summation, and a sixth step of calculating a continuous time by summing up the walking periods when the walking period determined to be the walking state continues, When the continuous time exceeds the predetermined time, the average vector of acceleration vector data collected at least during the latest walking period is calculated, and the average vector is determined as the gravitational acceleration vector. It is characterized in that it comprises a seventh step of.

  Further, the body state detection method (3) according to the present invention is based on the direction of the gravitational acceleration vector in the body state detection method (2), and each acceleration is determined until the gravity acceleration vector is determined next. The method further includes an eighth step of calculating an angle of the vector data and determining the angle as a tilt angle of the body wearing the acceleration sensor.

  Further, the physical condition detection method (4) according to the present invention is the above-described physical condition detection method (1), wherein the second step is the absolute value of each acceleration vector with respect to the acceleration vector data collected continuously. A fourth step of calculating a value, an average value of these absolute values, a standard deviation, and a periodicity, and when the standard deviation is within a predetermined range and the periodicity is greater than a predetermined value, And a fifth step of judging.

  Further, the body state detection method (5) according to the present invention is the body state detection method (4) described above, wherein the fifth step is performed on the acceleration vector data during the period in which the walking state is determined. Component analysis is performed, and the method includes a sixth step using the first principal component, the second principal component, and the third principal component as the body axis.

  Further, in the physical condition detection method (6) according to the present invention, in the physical condition detection method (5), the fifth step obtains an average acceleration vector from the acceleration vector data as the target of the principal component analysis. , A seventh step of determining the direction of the first principal component close to the average acceleration vector as a direction directly below the body, and a time change of the component of the second principal component direction of the acceleration vector data, An eighth step of determining a forward direction; and a ninth step of determining a direction of the third principal component orthogonal to the right-down direction and the forward direction as a right-hand direction of the body. It is said.

  The body state detection method (7) according to the present invention is the body state detection method (6), wherein the eighth step assumes that one direction of the second main component is a positive direction, and the acceleration When the time change of the component in the second principal component direction of the vector data is similar to the whole or a part of the sawtooth wave having a gently decreasing portion, the one direction of the second principal component is set as the body advance direction. And the time change of the component in the second principal component direction of the acceleration vector data is similar to the whole or a part of a sawtooth wave having a slowly increasing portion, the one of the second principal components. It is characterized by the step of determining the direction opposite to the direction as the forward direction of the body.

Further, the physical condition detection method (8) according to the present invention is the physical condition detection method (6) or (7), wherein the third step is a tenth step of obtaining an average acceleration vector of the acceleration vector data. And an eleventh step of obtaining an average acceleration vector and an angle formed by each of the right-down direction, the forward direction, and the right-side direction to obtain a tilt angle of a body wearing the acceleration sensor. Yes.

  In addition, the body condition detection program (1) according to the present invention uses the three-axis acceleration sensor in a body condition detection apparatus including a three-axis acceleration sensor attached to the body, a data collection unit, and a processing unit. A first function for collecting acceleration vector data at a predetermined sampling interval, a second function for detecting the walking state of the body using the acceleration vector data continuously collected, and within a period of the walking state And a third function for determining a gravitational acceleration vector or a body axis using the continuous acceleration vector data.

  In addition, the body condition detection program (2) according to the present invention is the absolute value of each acceleration vector in the body condition detection program (1), in which the second function relates to the acceleration vector data collected continuously. A fourth function for calculating a value, an average value of these absolute values, and a standard deviation, a number of times the standard deviation, the absolute value exceeds the average value, and a time when the absolute value exceeds the average value A fifth function for detecting the walking state using the sum, and a sixth function for calculating a continuous time by summing up the walking periods when the walking period determined to be the walking state continues, When the continuous time exceeds a predetermined time, calculate an average vector of acceleration vector data collected at least during the latest walking period, and determine the average vector as a gravitational acceleration vector It is characterized in that it comprises a seventh function.

  Further, the body condition detection program (3) according to the present invention uses the direction of the gravity acceleration vector in the body condition detection program (2) as a reference, and then determines each acceleration until the gravity acceleration vector is determined. An eighth function of calculating an angle of vector data and determining the angle as a tilt angle of a body wearing the acceleration sensor is further realized.

  Further, the body condition detection program (4) according to the present invention is the absolute value of each acceleration vector in the body condition detection program (1), in which the second function is related to the acceleration vector data collected continuously. A fourth function for calculating a value, an average value of these absolute values, a standard deviation, and periodicity, and when the standard deviation is within a predetermined range and the periodicity is greater than a predetermined value, And a fifth function to be determined.

  Further, the body condition detection program (5) according to the present invention is based on the acceleration vector data during the period in which the fifth function is determined to be the walking state in the body condition detection program (4). An analysis is performed, and a sixth function having the first principal component, the second principal component, and the third principal component as the body axis is included.

  Further, in the body condition detection program (6) according to the present invention, in the body condition detection program (5), the fifth function obtains an average acceleration vector from the acceleration vector data subjected to the principal component analysis. , According to a seventh function for determining the direction of the first principal component close to the average acceleration vector as a direction directly below the body, and a time change of the component of the second principal component direction of the acceleration vector data. And an eighth function for determining a forward direction, and a ninth function for determining the direction of the third principal component orthogonal to the right-down direction and the forward direction as a right-hand direction of the body. It is said.

Further, in the body condition detection program (7) according to the present invention, in the body condition detection program (6), the eighth function assumes that one direction of the second principal component is a positive direction, and the acceleration When the time change of the component in the second principal component direction of the vector data is similar to the whole or a part of the sawtooth wave having a gently decreasing portion, the one direction of the second principal component is set as the body advance direction. And the time change of the component in the second principal component direction of the acceleration vector data is similar to the whole or a part of a sawtooth wave having a slowly increasing portion, the one of the second principal components. It is characterized by the function of determining the direction opposite to the direction as the body's forward direction.

  Further, in the body condition detection program (8) according to the present invention, in the body condition detection program (6) or (7), the third function is a tenth function for obtaining an average acceleration vector of the acceleration vector data. And an eleventh function for determining an angle formed between the average acceleration vector and each of the right-down direction, the forward direction, and the right-side direction to obtain a tilt angle of a body wearing the acceleration sensor. Yes.

  According to the present invention, it is not necessary to make a pre-adjustment such as accurately mounting the triaxial acceleration sensor before calibration or calibrating the mounting direction, and also when the mounting direction of the acceleration sensor changes during the measurement. It is possible to accurately detect a physical state such as a walking state and a posture inclination.

  In particular, the direction of the average acceleration vector calculated from the acceleration data of the period detected as the walking state is used as the direction of the gravitational acceleration as a reference for obtaining the body inclination angle, thereby accurately calculating the body inclination angle. be able to.

  In addition, the principal component analysis is performed on the acceleration data in the walking state, and the positive direction of the body axis is considered by considering the relationship between the first principal component and the average acceleration vector and the temporal change pattern of the acceleration component in the second principal component direction. Can be determined with high accuracy. By using this body axis, the posture can be determined by accurately calculating the tilt angle of the body.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, embodiments of the invention will be described with reference to the accompanying drawings.

  FIG. 1 is a block diagram showing a schematic configuration of a body state detection device according to an embodiment of the present invention. The body state detection apparatus according to the present embodiment samples a triaxial acceleration sensor (hereinafter referred to as an acceleration sensor) 1 attached to a human body and an analog output signal of the acceleration sensor 1 at a predetermined time interval. A data sampling unit 2 that performs D conversion and outputs a digital signal, a recording unit 3 that records a digital signal from the data sampling unit 2, a memory unit 4, and a processing unit 5 that controls these units are provided. For example, when the acceleration sensor 1 is attached to the lower back of a human body and receives an external force, the acceleration sensor 1 is an analog corresponding to the acceleration in each of three orthogonal axes (X axis, Y axis, Z axis) set in advance in the acceleration sensor 1. A signal (such as a voltage) is output from three corresponding output terminals (not shown).

Under the control of the processing unit 5, the data sampling unit 2 samples each analog signal output from the acceleration sensor 1 at a predetermined sampling interval Δt, performs A / D conversion, and converts the digital acceleration data (g _x , (g _y , g _z ) are output in the order of collection. In the following description, unless otherwise specified, the acceleration data means three-dimensional vector data (g _x , g _y , g _z ). The output acceleration data is recorded in the recording unit 3 in time series. In addition to the control of each unit, the processing unit 5 performs acceleration processing recorded in the recording unit 3 as a target, and executes a walking state detection process, which will be described later, using the memory unit 4 as a work area.

Hereinafter, each function of the physical condition detection apparatus according to the present embodiment will be specifically described.
(Walking state detection function)
FIG. 2 is a flowchart showing walking state detection processing by the body state detection device according to the present embodiment. Here, it is assumed that acceleration data from the acceleration sensor 1 attached to the waist of the human body is collected for a predetermined period at a predetermined sampling interval Δt and recorded in the recording unit 3 in time series in advance. It becomes a processing target.

  FIG. 3 shows an example of recorded acceleration data. In FIG. 3, the vertical axis represents the uniaxial component of the acceleration data, and the horizontal axis represents time. In the following description, the processing performed by the processing unit 5 will be described unless otherwise specified. Further, in the processing at each step, the processing unit 5 appropriately reads acceleration data from the recording unit 3 to the memory unit 4, performs calculation using a predetermined area of the memory unit 4 as a work area, and records the result as appropriate. It will be recorded in part 3.

In step S1, initialization is performed. Evaluation time width T ₁ , shift time width ΔT ₁ (ΔT
₁ ≦ T _1), sampling interval Delta] t, the upper limit value sigma _max and the lower limit value sigma _min of the standard deviation sigma to be used in the determination step to be described later, the upper limit value n _max and the lower limit value n _min of the number parameter, the upper limit t of the time parameter Predetermined values are set for _max and the lower limit t _min . Further, “0” is set to the repetition counter k, and “0” is set to all the flags flag (j) (j is an integer value of 0 or more). As will be described later, since the acceleration data to be processed once is determined from all the time-series acceleration data while shifting by the shift time width ΔT ₁ along the time axis, the flag flag ( j) is provided.

In step S2, the evaluation time width T ₁ from the beginning of the acceleration data recorded in time series.
Acceleration data (g _xi , g _yi , g _zi ) (i = k to k + N ₁ −1) is read out. Evaluation acceleration data number N ₁ for every time width T ₁ is calculated by T ₁ / Δt.

In step S3, absolute values | g | _i = (g _xi ² + g _{y i} ² + g _zi ² ) ^1/2 of the acceleration data (g _xi , g _yi , g _zi ) read out in step S2 are calculated, and they are calculated. Average value g _av and standard deviation σ are calculated.

In step S4, the standard deviation σ calculated in step S3 is
σ _min <σ <σ _max (Formula 1)
It is determined whether or not the above is satisfied. If not satisfied, the process proceeds to step S5, where the value obtained by adding Δk to the counter k is set as a new counter k, and the above steps S2 to S4 are repeated. If it is determined that the condition is satisfied, the process proceeds to step S6. Here, increasing the counter k by Δk means shifting the shift time width ΔT ₁ along the time axis to determine the head of acceleration data to be processed next, and Δk = ΔT ₁ / Δt. It is. Therefore, Δk ≦ N ₁ .

In step 6, the number of times n that the absolute value | g | _{i of} the acceleration data obtained in step S 3 exceeds the average value g _av and the total time t ₁ where | g | _i > g _av are calculated.

In step S7, the number n and the total time t ₁ obtained in step S6 are
n _min <n <n _max (Formula 2)
t _min <t ₁ <t _max (Formula 3)
It is determined whether or not the above is satisfied. If it is determined that at least one of Expression 2 and Expression 3 is not satisfied, the process proceeds to Step S5, and then the processes of Steps S2 to S6 are repeated.

When it is determined that the relationship of Expression 2 and Expression 3 is satisfied, the process proceeds to Step S8, and “1” representing the walking state is set in the flag flag (j) corresponding to the section (time width T ₁ ). The flag flag (j) set here is, for example, a section (k to k + N ₁ − corresponding to the time width T _1.
It applies to the end of 1), ie k + N ₁ −1.

In step S9, it is determined whether or not the acceleration data to be processed remains N ₁ or more,
Steps S2 to S7 are repeated after returning to step S5 until it is determined that there is no remaining.

  Through the above processing, the values of all flag (j) are determined, and the section where flag (j) = 1 can be determined as the section in the walking state.

As an example, the evaluation time width T ₁ = 3 (seconds), the shift time width ΔT ₁ = 1 (second), and the average amplitude of the acceleration vector in the evaluation time width T ₁ is g _av σ _min = 0.2 g _av , σ _max = 0.9
g _av , n _min = 0.2 T ₁ / Δt, n _max = 0.8 T ₁ / Δt, t _min = 0.2 T ₁ , t _max =
FIG. 4 shows the result of applying the series of processes shown in FIG. 2 under the condition of 0.8T ₁ . In FIG. 4, acceleration data for X, Y, and Z indicate data (each component of three axes) collected at a sampling interval Δt of about 0.0586 (seconds) (17.065 Hz). The determination result is shown. In the walking determination result, the black belt portion indicates flag (j) = 1, that is, the walking state. The uppermost part of FIG. 4 shows the result of visual observation of the actual state of the human body. Here, two states of “sitting” and “walking” were repeated.

  Moreover, the attachment angle to the human body of the acceleration sensor was changed intentionally in each period of trials 1-3. 5A to 5C show a photograph in a state where the acceleration sensor 1 is attached to the waist and the directions of the three axes (X axis, Y axis, Z axis) of the acceleration sensor 1. FIG. 5 shows an angle formed by the vertically downward direction V and the X axis which is one axis of the triaxial acceleration sensor 1. In the period of trial 1, the acceleration sensor is mounted as usual, and the X axis of the acceleration sensor is inclined about 10 degrees from the vertical axis V (see FIG. 5A). In the period of trials 2 and 3, the acceleration sensor 1 is attached with an inclination, and the X axis of the acceleration sensor 1 is inclined by about 30 degrees and about 90 degrees from the vertical axis V (FIG. 5B). (See (c)).

  Comparing the uppermost stage and the lowermost stage in FIG. 4, the period marked “walking” in the uppermost stage and the period of the black belt in the lowermost stage correspond well, and the angle at which the acceleration sensor 1 is attached to the body It can be seen that the walking state can be detected well without depending on.

(Gravity acceleration direction detection function)
Next, a function for detecting the direction of gravity acceleration will be described. This is a function of automatically detecting a walking state from acceleration data, obtaining an average of acceleration data in the walking state, and determining this as a gravitational acceleration direction. In general, the average body posture in the walking state is considered to be maintained by the same person with respect to the gravitational acceleration direction. Therefore, from the acceleration data obtained by the acceleration sensor, the walking state is detected by the process shown in FIG. 2, and the walking state continues longer than a predetermined determination time width (hereinafter referred to as gravity acceleration determination time). If it is, the average acceleration data in the evaluation time width T ₁ can be the gravitational acceleration.

  FIG. 6 is a flowchart showing the detection process of the gravitational acceleration direction by the body state detection device according to the present embodiment. Similar to the description with reference to FIG. 2, the processing shown in FIG. 6 is also described as processing performed by the processing unit 5 unless otherwise specified, with respect to acceleration data recorded in advance in the recording unit 3 in time series.

Since the process of step S11-step S18 is the same as the process in step S1-S8 shown in FIG. 2, description is abbreviate | omitted. However, as the initial setting in step S11, to set the gravitational acceleration determination time T _2, different from the step S1.

In step S19, a continuous section L (continuous section in which all flag (j) values are “1”) in which the flag is set to flag (j) = 1 in step S18 is obtained, and the time length of the continuous section L is obtained. seek t _2.

In step S20, the total time t ₂ obtained in step S19, it is determined whether or not greater than the gravitational acceleration determination time T ₂ set in step S11. If it is determined that t ₂ > T ₂ is not satisfied, the process proceeds to step S15, and then the processes of steps S12 to S19 are repeated. t ₂ >
If it is determined that T ₂ , the process proceeds to step S21.

In step S21, an average vector of acceleration data in the last evaluation time width T ₁ in the continuous section L is obtained, and the result is recorded as gravitational acceleration after the continuous section L. Accordingly, the gravitational acceleration determined in the previous process is applied to the section in which this step S21 is not executed.

In step S22, the acceleration data to be processed is determined whether there remains N ₁ or more, until it is determined that no remaining, after returning to step S15, and repeats the processing of step S2～S21.

As described above, the gravitational acceleration direction over the entire period can be determined in the coordinate system of the acceleration sensor using the average of the acceleration data in the section (time width T ₁ ) in the walking state and satisfying the condition of t ₂ > T _2. it can.

As an example, for the measured acceleration data shown in FIG. 4, the evaluation time width T ₁ = 3 (seconds)
FIG. 7 shows the result of applying the series of processing shown in FIG. 6 with the shift time width ΔT ₁ = 1 (second) and the gravitational acceleration determination time T ₂ = 5 (second). In this example, in order to perform a more stable detection of the direction of gravitational acceleration, the detection condition of the walking state is set to a stricter condition than the above-described example, that is, σ _min
= 0.2 g _av , σ _max = 0.9 g _av , n _min = 0.275T ₁ / Δt, n _max = 0.725T ₁ / Δt, t _min = 0.365T ₁ , t _max = 0.635T ₁ did. In FIG. 7, the X axis,
The Y-axis and Z-axis acceleration data indicate actual measurement data, and the bottom line indicates the angle between the gravitational acceleration direction and the axis set in the acceleration sensor.

  In FIG. 7, the angle at which the acceleration sensor is attached to the human body is intentionally changed three times (each period is described as trials 1 to 3 in the top row). The state was repeated. As shown in FIG. 5, the acceleration sensor is attached to the human body during each period of trials 1 to 3 with the X axis of the acceleration sensor inclined about 10 degrees, about 30 degrees, and about 90 degrees from the vertical axis V, respectively. It has been.

When the uppermost stage and the lowermost stage in FIG. 7 are compared, the values (about 10 °, about 30 ° and about 90 °) shown in the uppermost stage are well reproduced (13 ° and 32.5 ° shown in the lowermost stage). And 90.7 °). In addition, when the walking state continues for a predetermined gravitational acceleration determination time T ₂ after the mounting angle of the acceleration sensor is changed, it is understood that the gravitational direction can be calculated immediately (in the rightmost frame in FIG. The staircase marked "Renewal of"). That is, it can be seen that the gravitational acceleration direction can be satisfactorily detected without depending on the angle at which the acceleration sensor is attached to the body.

(Body posture detection function)
Finally, the function for detecting the tilt angle of the body will be described. This is a function of detecting the correct body inclination angle without being influenced by the direction in which the acceleration sensor is mounted using the gravity acceleration direction detected by the gravity acceleration detection function described above.

That is, first, based on the gravitational acceleration direction updated by the gravitational acceleration detection function described in FIG. 7, the average acceleration vector (body acceleration vector) in the evaluation time width T ₁ of the acceleration data collected from the acceleration sensor at each time is obtained. Calculate the resulting angle. Then, the obtained angle is determined as the inclination angle of the body to which the acceleration sensor is attached (the inclination angle of the waist when attached to the waist).

  As an example, accelerations obtained by repeating “sitting” and “walking” by changing the direction of the acceleration sensor shown in FIGS. 4 and 7 into three types (about 10 °, about 30 °, and about 90 °). The result of applying this function to the data is shown in FIG. In FIG. 8, the actual state is shown at the top, the three-axis acceleration data sampled below is shown, and “corrected” is written below it, and the inclination angle of the body by the body posture detection function is shown. The calculation result is shown. The bottom row labeled “No correction” indicates the body inclination angle obtained by processing the acceleration data without detecting the gravitational acceleration direction by the gravitational acceleration detection function.

  As shown in the stage marked “with correction”, according to the body posture detection function, the inclination angle of the body at the time of sitting is substantially constant even if the mounting angles of trials 1 to 3 and the acceleration sensor are changed. It can be seen that the value of (about 23 degrees to about 37 degrees) can be calculated. On the other hand, in the stage marked “no correction”, since the correction by detecting the direction of gravitational acceleration is not performed, the inclination angle of the body greatly changes depending on the mounting angle of the acceleration sensor. Therefore, it turns out that the detection function of this body posture is very effective.

(Highly accurate body posture detection function)
In the above description, the number of times that the absolute value of acceleration | g | _i exceeds the average acceleration value g _AV is used for detecting the walking state, but the walking speed may affect the detection accuracy. Further, the inclination angle of the body cannot be detected as an inclination angle with respect to each of the front, lower, and side directions of the body, and for example, it cannot be determined whether the front is all or whether it is inclined sideways. In order to improve these points and detect the body posture more accurately, the stable walking state is detected, the body axis in this state is obtained, and the body axis and the average acceleration vector obtained by the acceleration sensor are formed. The angle is determined as the body tilt angle. This will be specifically described below.

  FIG. 9 is a flowchart showing a highly accurate body posture detection process by the body state detection device shown in FIG. Similarly to the description related to FIG. 2, the processing shown in FIG. 9 is also described as processing performed by the processing unit 5 unless otherwise specified, with acceleration data recorded in advance in the recording unit 3 in time series.

The processes in steps S31 and S32 are the same as the processes in steps S1 and S2 shown in FIG. However, the initial setting in step S31 is different from step S1 in that an inclination angle calculation time T ₃ (<T ₁ ) and a reference value for determination to be described later are set.

In step S33, similarly to step S3 shown in FIG. 2, the absolute value | g of each acceleration data (g _xi , g _yi , g _zi ) (i = k to k + N ₁ −1) read in step S32
_I = (g _xi ² + g _yi ² + g _zi ² ) ^1/2 is calculated, and an average value g _av and a standard deviation σ thereof are calculated.
Further, the periodicity ν of the temporal variation of the absolute value | g | _i of the acceleration data is calculated.
The reason for obtaining the periodicity ν is to detect a stable walking state, and processing described later is performed based on the characteristics of this state.

The periodicity ν of the acceleration data absolute value | g | _i that is the time series data of the time interval Δt in the evaluation time width T ₁ can be obtained using a known technique. For example, | g | _i the Fourier transform, depending on the degree peak broadening of the resulting frequency spectrum, it is possible to determine the periodicity [nu. Further, a frequency distribution of time intervals in which | g | _i exceeds the average value g _av is obtained, and the ratio of the maximum value of the cumulative frequency in the predetermined time width ΔTc to the total number in the frequency distribution is also defined as the periodicity ν. Good.

In step S34, it is determined whether it is a walking state using the standard deviation (sigma) and periodicity (nu) calculated | required by step S33. Specifically, if σ _still ≦ σ ≦ σ _walk and ν> ν _walk , it is determined that the user is in the walking state, and the process proceeds to step S35. Otherwise, it is determined that the user is not in the walking state, and the process proceeds to step S38. . σ _still , σ _walk , and ν _still are reference values set in the initial setting in step S31.

In step S35, it is determined whether or not the walking state is stable under conditions stricter than those in step S34. That is, if σ _w0 ≦ σ ≦ σ _w1 and ν> ν _w0 , it is determined that the walking state is stable, and the process proceeds to step S36. Otherwise, it is determined that the walking state is not stable and the process proceeds to step S39. To do. Where σ _still <σ _w0 , σ _w1 ≦ σ _walk , ν _walk <ν _w0
It is.

In step S36, principal component analysis is performed on the acceleration data (g _xi , g _yi , g _zi ) (i = k to k + N ₁ −1) read out in step S32 to obtain a body axis. In a stable walking state, there is a characteristic that the same posture is maintained by the same person with respect to the direction of gravity. Therefore, the first to third principal components obtained by principal component analysis on the acceleration vector in a stable walking state are taken as body axes. That is, the first to third principal components are respectively the vertical direction (gravity acceleration direction), the front-rear direction, and the horizontal direction of the body during walking.

  In step S37, from the change in the acceleration component in the body axis (first to third principal components) direction obtained in step S36, the direction directly below the body, the forward direction (forward direction), and the right lateral direction ((( a) see) is recorded and recorded in the recording unit 3. When the first to third principal components obtained in step S36 are used as body axes, there are two directions in the positive direction of each axis. In other words, the positive and negative directions of the body axis cannot be uniquely determined only by principal component analysis. Therefore, first, the average acceleration vector of the walking state is obtained, and the direction of the first principal component close to the direction of the average acceleration vector is determined as the direction directly below the body. Next, an acceleration component in the second principal component direction is obtained with one direction of the second principal component as a provisional positive direction, and the front-rear direction of the body is determined based on the time change pattern of the obtained acceleration component. Specifically, as shown in FIG. 11, the degree of similarity between two sawtooth waveforms that are symmetrical with respect to the time axis is determined. One of the two sawtooth waves gradually decreases and then increases sharply and then decreases again gently (for example, the waveform indicated as “forward component” in FIG. 11), and the other increases gradually and then sharply increases. After that, it gradually increases again (for example, a waveform indicated as “reverse forward direction component” in FIG. 11). The middle waveform in FIG. 11 is the acceleration component in the second principal component direction, which is a waveform similar to the “advance direction component”. Therefore, the positive direction of the second principal component is defined as the front direction (forward direction) of the body. decide. On the other hand, when the acceleration component in the two principal components direction is similar to the sawtooth wave in the reverse advance direction component, the negative direction of the second principal component is determined as the forward direction (forward direction) of the body. Finally, the direction of the third principal component close to the direction orthogonal to the determined right-down direction and the forward direction in the right hand system is determined as the right lateral direction of the body. Note that the similarity may be calculated using a known matching technique. For example, the degree of similarity may be determined by obtaining a correlation between the waveform of the acceleration component in the second principal component direction and a sawtooth waveform as a determination reference. Also, as a method of determining the similarity to the sawtooth wave, paying attention to the difference in feature quantity (for example, differential coefficient) of two sawtooth waveforms symmetric with respect to the time axis, the same feature regarding the waveform of the acceleration component in the second principal component direction A quantity (differential coefficient) may be calculated and used for determination. Also, instead of the similarity (including the feature amount) with the entire sawtooth wave, a part of the sawtooth wave, for example, a portion where the acceleration changes sharply due to kicking of the foot or a portion where the acceleration gradually decreases The similarity (including the feature amount) may be used as a criterion for determination.

In step S38, the body state other than the walking state is determined using the standard deviation σ and the periodicity ν calculated in step S33. Specifically, σ _walk <σ ≦ σ _shock and ν> ν _walk
If it is "running state", if σ <σ _still , "still state", if σ> σ _shock , "impact state" such as falling, and if neither of these states nor walking state Is determined as “other state”.

In step S39, when the determination result in physical condition in step S38 is other than "impact state", the acceleration vector of the average calculated for each tilt angle calculation time T _3, reads the information of the body axis from the recording unit 3 The angle (body inclination angle) formed by the average acceleration vector and the body axis is obtained. That is, as shown in FIG. 10B, three angles θ, φ, and γ that the average acceleration vector forms with each body axis (downward direction, forward direction, right lateral direction) are obtained. Here, posture determination can also be performed from the obtained θ. For example, the reference value theta _0, theta _1, the theta ₂ is initialized in step S31, if θ ₀ <θ ≦ θ ₁ "tilted small", if θ ₁ <θ ≦ θ ₂ "in slope" If θ ≧ θ ₂ , it is determined that “the inclination is large”.

In step S40, it is determined whether or not the acceleration data to be processed remains N ₁ (corresponding to the evaluation time width T ₁ ) or more, and the process proceeds to step S41 until Δk (Δk = ΔT ₁ / Δt) is added as a new counter k, and the above steps S32 to S39 are repeated.

  As described above, the body axis can be accurately determined from the acceleration data in the stable walking state, and then the body inclination angle can be accurately determined as the angle formed by the determined body axis and the average acceleration vector. it can.

  Depending on the acceleration data to be processed, the process in step S39 may be performed without performing the processes in steps S36 and S37. In that case, a meaningful body tilt angle is not calculated because the body axis has not been determined. Therefore, a flag indicating whether or not the body axis has been calculated is provided, the state of the flag is determined in step S39, and if the body axis has not been calculated, the processing in step S39 described above may not be executed. . Alternatively, in the initial setting in step S31, an initial value in the body axis direction may be set. Furthermore, if the calculation result of the body tilt angle during a period when the body axis is not determined is excluded, the initial value in the body axis direction may not be set.

FIG. 12 shows an experimental result in which this function is applied to measured acceleration data. Four similar types of acceleration sensors are mounted on the front, back, left, and right of the human body without special positioning, walking, sitting on a chair, bending in the front direction, looking from the right, looking left A series of operations for peeping through were performed, acceleration data output from each acceleration sensor was recorded, and a series of processing shown in FIG. 9 was performed on this data. Evaluation time width T ₁ = 5 (seconds)
Inclination angle calculation time T ₃ = 1 (second). FIG. 12 shows data related to acceleration sensors attached to the front side, the left side, the rear side, and the right side from the top. The signal waveform of each stage shown as “(a) 3-axis acceleration information” on the left side of FIG. 12 represents the XYZ-axis acceleration components acquired from the acceleration sensor, and shown as “(b) body inclination angle” on the right side. The signal waveforms represent body inclination angles θ, φ, γ (actually, θ, φ-90, γ-90) calculated using the determined body axis. A part of the body inclination angle is added numerically. Comparing the data of each stage of body tilt angle, this function can obtain almost equal body tilt angle regardless of the mounting position and mounting direction of the acceleration sensor on the body, and seated and bent into the front It can be seen that the peep from the right and the peep from the left can be identified.

  In the above description, the body state detection apparatus having the configuration shown in FIG. 1 has been described.

In addition, all the components shown in FIG. 1 may be incorporated into one unit, and the unit may be attached to the body. The acceleration sensor and the data collection unit may be incorporated into one unit, and only that unit is attached to the body. May be. In the former case, the processing result may be wirelessly transmitted to another processing apparatus by wireless communication, for example. In the latter case, the digital data output from the data collection unit may be wirelessly transmitted to the recording unit by wireless communication, for example.

  Further, the processes shown in FIGS. 2, 6, and 9 can be executed with various changes such as changing the order of the steps. For example, in FIG. 9, the walking state detection process in step S34 may be included in step S38. In that case, the high periodicity detection process of step S35 is first performed. Further, in the flowchart of FIG. 9, steps S32 to S35 (using periodicity) may be replaced with steps S12 to S20 (using the number of times that the absolute value of the acceleration data exceeds the average value) in FIG.

  Further, the direction of the body axis is not limited to the downward direction, the forward direction, and the right lateral direction, and may be the opposite direction or a left-handed axis.

In the above-described walking state detection function, the flag flag (j) at time t + T ₁ is determined using acceleration data within the evaluation time width T ₁ (time t to t + T ₁ ). However, the present invention is not limited to this. , Flag flag (j) at an arbitrary time t + τ in the section from t to t + T ₁ may be determined. Here, τ <T ₁ . In that case, when the shift time width ΔT ₁ is taken into consideration, the determined flag flag (j) is maintained during the time t + τ to t + τ + ΔT ₁ . For example, τ = T _1/2 .

  In the walking state detection function described with reference to FIG. 2, the case where the average value and the standard deviation of the acceleration data are used for the determination of the walking state has been described. This is the detection of periodicity described with reference to FIG. It is included in the concept of the method, and various methods can be used for detecting the periodicity as described above. For example, with respect to acceleration data for each predetermined period, absolute value frequency analysis may be performed by Fourier transform or the like, and a walking state or a stable walking state may be determined based on a ratio of frequency components included. Moreover, you may perform a frequency analysis for every component instead of the absolute value of acceleration data.

In the above gravity acceleration direction detection function, the case where the average acceleration vector in the latest evaluation time width T ₁ section, that is, the last evaluation time width T ₁ section of the continuous section L is used as the gravity acceleration vector has been described. However, the present invention is not limited to this. For example, the average acceleration vector in the continuous section L may be used as the gravitational acceleration vector.

  In the above-described walking state detection function, gravity acceleration direction detection function, body posture detection function, and highly accurate body posture detection function, the case where acceleration data recorded in advance in the recording unit is processed has been described. However, processing may be performed in real time in parallel with data collection. In that case, for example, the process of step S2 of FIG. 2 showing the detection function of the walking state, the process of step S12 of FIG. 6 showing the detection function of the gravitational acceleration direction, and the step of FIG. 9 showing the highly accurate body posture detection function. The process of S31 may be a process of collecting a predetermined number of acceleration data.

Further, values such as σ _min , σ _max , n _min , n _max , t _min, and t _max are not limited to the values shown as examples above, and can be set as appropriate.

1 is a block diagram showing a schematic configuration of a body state detection device according to an embodiment of the present invention. It is a flowchart which shows the detection process of the walk state by the body state detection apparatus which concerns on embodiment of this invention. It is a wave form diagram which shows an example of the acceleration data of 1 axis direction. It is a figure which shows an example of the result of having applied the detection processing function of the walk state by the body state detection apparatus which concerns on embodiment of this invention. It is a figure which shows the state which attached the acceleration sensor to the body, and the direction of 3 axes | shafts of an acceleration sensor. It is a flowchart which shows the detection process of the gravity acceleration direction by the physical condition detection apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the result of having applied the detection function of the gravity acceleration direction by the physical condition detection apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the result of having applied the detection function of the body posture by the body state detection apparatus which concerns on embodiment of this invention. It is a flowchart which shows the detection process of the highly accurate body posture by the body state detection apparatus which concerns on embodiment of this invention. It is a figure which shows the relationship between the axis | shaft of an acceleration sensor, a body axis, and a gravitational acceleration direction, (a) shows the relationship between the XYZ axis | shaft of an acceleration sensor in a walking state, and a body axis, (b) is the state which bent forward. It is a figure which shows a body inclination angle. It is a figure explaining the method of determining a forward direction from the acceleration component of a 2nd main component direction. It is a figure which shows an example of the result of having applied the detection function of the highly accurate body posture by the body state detection apparatus which concerns on embodiment of this invention.

Explanation of symbols

1 3-axis acceleration sensor 2 Data collection unit 3 Recording unit 4 Memory unit 5 Processing unit

Claims (24)

A three-axis acceleration sensor worn on the body;
Data collection means for collecting acceleration vector data output from the three-axis acceleration sensor at a predetermined sampling interval;
Processing means,
The processing means is
Using the acceleration vector data collected continuously, detecting the walking state of the body,
A body state detection device, wherein a gravitational acceleration vector or a body axis is determined using continuous acceleration vector data within a period of the walking state. The processing means is
For the acceleration vector data collected continuously, the absolute value of each acceleration vector, the average value of these absolute values, and the standard deviation are calculated,
Detecting the walking state using the standard deviation, the number of times that the absolute value exceeds the average value, and the total time that the absolute value exceeds the average value,
If the walking period determined to be in the walking state continues, calculate the continuous time by summing those walking periods,
The average vector of acceleration vector data collected during at least the latest walking period is calculated when the continuous time exceeds a predetermined time, and the average vector is determined as a gravitational acceleration vector. The physical condition detection apparatus of description.   The processing means calculates the angle of each acceleration vector data based on the direction of the gravitational acceleration vector and then determines the gravitational acceleration vector until the gravitational acceleration vector is determined. The body state detection device according to claim 2, wherein the body state detection device is determined as an angle. The processing means is
Calculate the absolute value of each acceleration vector, the average value of these absolute values, the standard deviation, and the periodicity for the acceleration vector data collected continuously,
The body state detection device according to claim 1, wherein the walking state is determined when the standard deviation is within a predetermined range and the periodicity is larger than a predetermined value. The processing means is
5. The principal component analysis is performed on the acceleration vector data for the period determined to be the walking state, and the first principal component, the second principal component, and the third principal component are used as the body axis. The body condition detection apparatus as described in 2. The processing means is
An average acceleration vector is obtained from the acceleration vector data as the target of the principal component analysis,
Determining the direction of the first principal component close to the average acceleration vector as a direction directly below the body;
According to the time change of the component of the second principal component direction of the acceleration vector data, determine the body forward direction,
The body state detection device according to claim 5, wherein a direction of the third principal component orthogonal to the right-down direction and the forward direction in a right-handed system is determined as a right lateral direction of the body. In the determination of the body advance direction, the processing means,
Assuming that one direction of the second principal component is a positive direction, the time variation of the component of the second principal component direction of the acceleration vector data is similar to all or part of a sawtooth wave having a gradually decreasing portion. In the case, the one direction of the second principal component is determined as a body forward direction, and a sawtooth wave having a portion in which the time change of the component in the second principal component direction of the acceleration vector data gradually increases. The body state detection device according to claim 6, wherein when the whole or a part is similar, a direction opposite to the one direction of the second principal component is determined as a body advance direction. The processing means is
Obtaining an average acceleration vector of the acceleration vector data;
8. The inclination angle of the body wearing the acceleration sensor is obtained by calculating an angle formed between the average acceleration vector and each of the direct downward direction, the forward direction, and the right lateral direction. The physical condition detection apparatus of description. A first step of collecting acceleration vector data at a predetermined sampling interval using a three-axis acceleration sensor mounted on the body;
A second step of detecting the walking state of the body using the acceleration vector data collected continuously;
And a third step of determining a gravitational acceleration vector or a body axis using continuous acceleration vector data within a period of the walking state. The second step includes
A fourth step of calculating an absolute value of each acceleration vector, an average value of these absolute values, and a standard deviation with respect to the acceleration vector data collected continuously;
A fifth step of detecting the walking state using the standard deviation, the number of times the absolute value exceeds the average value, and the sum of the time when the absolute value exceeds the average value;
When the walking period determined to be the walking state continues, the sixth step of calculating the continuous time by summing those walking periods;
And a seventh step of calculating an average vector of acceleration vector data collected at least during the latest walking period and determining the average vector as a gravitational acceleration vector when the continuous time exceeds a predetermined time. The body condition detection method according to claim 9.   The angle of each acceleration vector data is calculated based on the direction of the gravitational acceleration vector, and then the gravitational acceleration vector is determined, and the angle is determined as the inclination angle of the body wearing the acceleration sensor. The body state detection method according to claim 10, further comprising 8 steps. The second step includes
A fourth step of calculating an absolute value of each acceleration vector, an average value of these absolute values, a standard deviation, and a periodicity with respect to the acceleration vector data collected continuously;
The physical condition detection method according to claim 9, further comprising a fifth step of determining the walking state when the standard deviation is within a predetermined range and the periodicity is greater than a predetermined value. The fifth step includes
A sixth step of performing a principal component analysis on the acceleration vector data for the period determined to be the walking state and using the first principal component, the second principal component, and the third principal component as the body axis. The body condition detection method according to claim 12. The fifth step includes
An average acceleration vector is obtained from the acceleration vector data subjected to the principal component analysis, and a direction of the first principal component close to the average acceleration vector is determined as a direction directly below the body.
Steps,
An eighth step of determining a forward direction of the body in accordance with a time change of the component of the second principal component direction of the acceleration vector data;
The physical state according to claim 13, further comprising a ninth step of determining the direction of the third principal component orthogonal to the right-down direction and the forward direction in the right-handed system as a right lateral direction of the body. Detection method. The eighth step includes
Assuming that one direction of the second principal component is a positive direction, the time variation of the component of the second principal component direction of the acceleration vector data is similar to all or part of a sawtooth wave having a gradually decreasing portion. In the case, the one direction of the second principal component is determined as a body forward direction, and a sawtooth wave having a portion in which the time change of the component in the second principal component direction of the acceleration vector data gradually increases. 15. The body state detection method according to claim 14, wherein when the whole or part of the body is similar, the direction opposite to the one direction of the second principal component is determined as a body advance direction. . The third step includes
A tenth step of obtaining an average acceleration vector of the acceleration vector data;
An eleventh step including obtaining an average acceleration vector and an angle formed by each of the direct downward direction, the forward direction, and the right lateral direction to obtain an inclination angle of a body wearing the acceleration sensor. The body condition detection method according to claim 14 or 15. A body state detection device comprising a three-axis acceleration sensor attached to the body, data collection means, and processing means,
A first function for collecting acceleration vector data at a predetermined sampling interval using the three-axis acceleration sensor;
A second function for detecting the walking state of the body using the acceleration vector data collected continuously;
A body condition detection program for realizing a gravitational acceleration vector or a third function for determining a body axis using continuous acceleration vector data within a period of the walking state. The second function is
A fourth function for calculating an absolute value of each acceleration vector, an average value of these absolute values, and a standard deviation with respect to the acceleration vector data collected continuously;
A fifth function for detecting the walking state using the standard deviation, the number of times the absolute value exceeds the average value, and the sum of the time over which the absolute value exceeds the average value;
When the walking period determined to be in the walking state continues, a sixth function for calculating the continuous time by summing those walking periods;
And a seventh function for calculating an average vector of acceleration vector data collected at least during the latest walking period and determining the average vector as a gravitational acceleration vector when the continuous time exceeds a predetermined time. The body condition detection program according to claim 17.   The angle of each acceleration vector data is calculated based on the direction of the gravitational acceleration vector, and then the gravitational acceleration vector is determined, and the angle is determined as the inclination angle of the body wearing the acceleration sensor. The body condition detection program according to claim 18, further realizing eight functions. The second function is
A fourth function for calculating an absolute value of each acceleration vector, an average value of these absolute values, a standard deviation, and a periodicity with respect to the acceleration vector data collected continuously;
The body condition detection program according to claim 17, further comprising a fifth function that determines the walking state when the standard deviation is within a predetermined range and the periodicity is greater than a predetermined value. The fifth function is
It includes a sixth function that performs principal component analysis on the acceleration vector data in the period determined to be the walking state and uses the first principal component, the second principal component, and the third principal component as the body axis. The body condition detection program according to claim 20. The fifth function is
A seventh function for obtaining an average acceleration vector from the acceleration vector data to be subjected to the principal component analysis, and determining a direction of the first principal component close to the average acceleration vector as a direction directly below the body;
An eighth function for determining a forward direction of the body according to a time change of the component of the second principal component direction of the acceleration vector data;
The body condition according to claim 21, further comprising a ninth function for determining the direction of the third principal component orthogonal to the right downward direction and the forward direction in the right hand system as a right lateral direction of the body. Detection program. The eighth function is
Assuming that one direction of the second principal component is a positive direction, the time variation of the component of the second principal component direction of the acceleration vector data is similar to all or part of a sawtooth wave having a gradually decreasing portion. In the case, the one direction of the second principal component is determined as a body forward direction, and a sawtooth wave having a portion in which the time change of the component in the second principal component direction of the acceleration vector data gradually increases. 23. The body condition detection program according to claim 22, wherein the body condition detection program has a function of determining a direction opposite to the one direction of the second principal component as a body advance direction when similar to the whole or a part. . The third function is
A tenth function for obtaining an average acceleration vector of the acceleration vector data;
And an eleventh function for obtaining an angle formed by the average acceleration vector and each of the right direction, the forward direction, and the right lateral direction to obtain a tilt angle of a body wearing the acceleration sensor. The body condition detection program according to claim 22 or 23.

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