JP2018038521A - Rehabilitation evaluation device, rehabilitation evaluation method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately evaluate a degree of recovery of a trainee and accurately grasp whether or not the trainee is recovering.SOLUTION: A rehabilitation evaluation device includes: evaluation point acquisition means for acquiring evaluation points acquired by evaluating balance ability of a trainee by performing a balance test; myoelectric potential acquisition means for acquiring a myoelectric potential of predetermined muscles of the trainee's muscles during the balance test; feature amount calculation means for calculating a muscle synergy feature amount indicating a cooperative state of the predetermined muscles on the basis of the myoelectric potential acquired by the myoelectric potential acquisition means; and display means for displaying, on a coordinate system, a regression model indicating a change in coordinates of the muscle synergy feature amount and the evaluation points when the trainee is recovering, and coordinates of the calculated muscle synergy feature amount and the acquired evaluation points.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a rehabilitation evaluation apparatus, a rehabilitation evaluation method, and a program for evaluating a recovery degree of a trainer through rehabilitation training.

  A rehabilitation evaluation apparatus for evaluating the degree of recovery of a trainer through rehabilitation training, based on the myoelectric potential of the trainer's muscles, and calculating muscle synergistic features that indicate the cooperative state of each muscle. A rehabilitation evaluation apparatus that displays synergistic feature quantities is known (for example, see Patent Document 1).

Japanese Patent Application Laid-Open No. 2015-073805

  In the rehabilitation evaluation apparatus, even if the muscle synergy feature value is acquired and displayed, it is difficult to estimate the final target value of the muscle synergy feature value. For this reason, it is unclear whether or not the muscle synergy feature value shows a recovery tendency. In addition, the myoelectric potential and the muscle synergistic feature amount may vary depending on the daily physical condition of the trainee. For this reason, it is difficult to accurately evaluate the degree of recovery of the trainee.

  The present invention has been made to solve such problems, a rehabilitation evaluation apparatus that can accurately evaluate the degree of recovery of the trainee and can accurately grasp whether the trainee is in a recovery tendency, The main purpose is to provide a rehabilitation evaluation method and program.

In order to achieve the above object, one embodiment of the present invention provides:
A rehabilitation evaluation apparatus for evaluating a recovery degree of a trainer by rehabilitation training,
An evaluation score acquisition means for acquiring an evaluation score obtained by performing a balance test and evaluating the balance ability of the trainee;
At the time of the balance test, a myoelectric potential acquisition means for acquiring a myoelectric potential of a predetermined muscle among the trainee's muscles;
Feature amount calculating means for calculating a muscle synergy feature amount indicating a cooperative state of the predetermined muscle based on the myoelectric potential acquired by the myoelectric potential acquiring means;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, the muscle synergy feature amount calculated by the feature amount calculation unit, and the Display means for displaying the coordinates of the evaluation points acquired by the evaluation point acquisition means;
It is a rehabilitation evaluation apparatus characterized by comprising.
In this aspect, attribute information acquisition means for acquiring attribute information indicating the attributes of the trainee, and points along the regression model based on the attribute information acquired by the attribute information acquisition means, and the training Target point setting means for setting at least one target point that the person wants to target, and the display means has the muscle synergy feature calculated by the regression model and the feature value calculating means on the coordinate system. The amount, the coordinates of the evaluation points acquired by the evaluation point acquisition means, and the target points set by the target point setting means may be displayed.
In this aspect, it further comprises storage means for storing the coordinates of the muscle synergy feature quantity calculated by the feature quantity calculation means and the evaluation point acquired by the evaluation point acquisition means, and the display means comprises the storage Based on past muscle synergy feature values and evaluation points stored by the means, the locus of changes in the coordinates of the muscle synergy feature values and the evaluation points may be displayed on the coordinate system.
In this aspect, the display unit further includes an estimation unit that estimates a future muscle synergy feature value and an evaluation point coordinate based on a past muscle synergy feature amount and an evaluation point coordinate stored by the storage unit. On the coordinate system, along with the trajectory of the change in the coordinates of the muscle synergy feature and evaluation point, the current muscle synergy feature and the coordinate of the evaluation point, and the future muscle synergy feature estimated by the estimating means and The coordinates of the evaluation point may be displayed.
In this one aspect, the estimation means may include at least one of a time required for a unit change amount of a past muscle synergy feature amount stored by the storage means and a time required for a unit change amount of a past evaluation point. Based on this, the future muscle synergy feature value and the coordinates of the evaluation point may be estimated.
In this aspect, the image processing apparatus further includes update means for updating the regression model based on the muscle synergy feature quantity and the evaluation point coordinates stored by the storage means, and the display means is the regression updated by the update means. The model may be displayed.
In this aspect, the muscle synergy feature value and the coordinates of the evaluation points stored by the storage unit are the attributes of the trainee, the elapsed time from the training start of the trainer, the training content of the trainer, or the training. The degree of recovery of the trainee is classified for each, and based on the classified muscle synergy feature and the coordinates of the evaluation point, the trainer attributes, the elapsed time from the start of the trainer training, the trainer training A regression model generation unit that generates a regression model for each content, or the degree of recovery of the trainer, and the regression model, and the display unit generates a regression model corresponding to the trainer generated by the regression model generation unit. It may be displayed.
In order to achieve the above object, one embodiment of the present invention provides:
A rehabilitation evaluation method for evaluating a recovery degree of a trainer by rehabilitation training,
Performing a balance test to obtain an evaluation score that evaluates the balance ability of the trainee;
Obtaining a myoelectric potential of a predetermined muscle among the muscles of the trainer during the balance test; and
Calculating a muscle synergy feature amount indicating a cooperation state of the predetermined muscle based on the acquired myoelectric potential;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, and the calculated muscle synergy feature amount and the coordinate of the evaluation point, Steps to display;
It may be a rehabilitation evaluation method characterized by including.
In order to achieve the above object, one embodiment of the present invention provides:
A program for assessing the recovery level of trainees through rehabilitation training,
A process of performing a balance test to obtain an evaluation score that evaluates the balance ability of the trainee;
A process of calculating a muscle synergy feature amount indicating a cooperation state of the predetermined muscle based on a myoelectric potential of the predetermined muscle among the muscles of the trainee acquired at the balance test;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, and the calculated muscle synergy feature amount and the coordinate of the evaluation point, Processing to display,
It may be a program characterized by causing a computer to execute.

  According to the present invention, it is possible to provide a rehabilitation evaluation apparatus, a rehabilitation evaluation method, and a program that can accurately evaluate the recovery degree of the trainee and can accurately grasp whether or not the trainee has a recovery tendency.

It is a block diagram which shows schematic structure of the rehabilitation evaluation apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the change of the myoelectric potential of a rectus abdominis, a gluteus medius, a femoral stretcher, and an anterior tibial muscle. It is the figure which displayed the regression model, the current muscle synergy feature-value, and the coordinate of an evaluation point on the muscle synergy feature-value / evaluation point coordinate system. It is a flowchart which shows an example of the processing flow of the rehabilitation evaluation method which concerns on Embodiment 1 of this invention. It is a block diagram which shows schematic structure of the rehabilitation evaluation apparatus which concerns on Embodiment 2 of this invention. It is the figure which displayed the regression model, the current muscle synergy feature, the coordinates of the evaluation point, and the target point on the muscle synergy feature / evaluation point coordinate system. It is the figure which displayed the locus | trajectory of the change of a muscle synergy feature-value and the coordinate of an evaluation point on a muscle synergy feature-value / evaluation point coordinate system. It is a block diagram which shows schematic structure of the rehabilitation evaluation apparatus which concerns on Embodiment 4 of this invention. The muscle synergy feature / evaluation point coordinate system displays the locus of changes in the muscle synergy feature and evaluation point coordinates and the current muscle synergy feature and evaluation point coordinates along with the current muscle synergy feature and evaluation point coordinates. FIG. The figure which estimated the future muscle synergy feature-value and the coordinate of the evaluation point after a fixed time based on the time taken for the coordinate of a muscle synergy feature-value and the evaluation point to change from the point a to the point b (unit change amount) It is. It is a block diagram which shows schematic structure of the rehabilitation evaluation apparatus which concerns on Embodiment 5 of this invention.

Embodiment 1
Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of a rehabilitation evaluation apparatus according to Embodiment 1 of the present invention. The rehabilitation evaluation apparatus 1 according to the first embodiment is for evaluating the degree of recovery of a trainer by rehabilitation training.

  The rehabilitation evaluation apparatus 1 includes an evaluation point acquisition unit 2 that acquires an evaluation point that evaluates the balance ability of a trainee, a myoelectric potential acquisition unit 3 that acquires a myoelectric potential of the trainee's muscle, and a cooperative state of the trainer's muscle And a display unit 5 for displaying the muscle synergy feature amount and the coordinates of the evaluation points.

  The rehabilitation evaluation apparatus 1 includes, for example, a CPU (Central Processing Unit) that performs arithmetic processing, an arithmetic program executed by the CPU, a ROM (Read Only Memory) and a RAM (Random Access Memory) that store various data. The hardware is composed mainly of a microcomputer including a memory and an interface unit (I / F) for inputting / outputting signals to / from the outside. The CPU, memory, and interface unit are connected to each other via a data bus or the like.

  The evaluation score acquisition unit 2 is a specific example of evaluation score acquisition means. The evaluation score acquisition unit 2 acquires, for example, an evaluation score that evaluates the balance ability of the trainee based on the posture change of the trainer when the balance test is performed.

  More specifically, the evaluation score acquisition unit 2 performs a balance test in which a trainee is lifted up with a belt or the like while standing on a floor board and the board is moved by a certain amount in the left-right direction or the front-back direction. Based on the posture of the trainer at that time, the balance of the trainer is evaluated.

In this balance test,
(A) When both hands and legs of the trainee do not move from the initial position, the evaluation score is +2.
(B) When only the trainee's hand moves from the initial position, the evaluation score is +1.
(C) When only the trainee's foot moves from the initial position, the evaluation score is -1.
(D) When the trainee's hands and feet move from the initial position, the evaluation score is -2.

  For example, a marker or the like is attached to both hands and feet of the trainee, and the movement of the marker is photographed with a camera. The evaluation score acquisition unit 2 detects the movement of both hands and feet of the trainee based on the image of the trainee's limbs taken by the camera, and calculates the evaluation score. The trainee may stand on a belt of a dreadmill, an inverted motorcycle, a pedal-type walking training machine, etc., and move in the left-right direction or the front-rear direction. The calculation method of the evaluation score is an example, and is not limited to this. The evaluation score acquisition unit 2 may calculate an evaluation score that evaluates the balance ability of the trainee based on, for example, a change in pressure on the left and right soles of the trainee and a change in the movement of the center of gravity. Further, the user may calculate the evaluation score and input it to the evaluation score acquisition unit 2.

  The myoelectric potential acquisition unit 3 is a specific example of myoelectric potential acquisition means. The myoelectric potential acquisition unit 3 acquires the myoelectric potential of a predetermined muscle among the muscles of the trainee during the balance test. It is preferable that the myoelectric potential acquisition unit 3 acquires myoelectric potentials of at least the rectus abdominis muscle, the gluteus medius, the femoral stretcher muscle, and the anterior tibial muscle. The myoelectric potential acquisition unit 3 acquires the myoelectric potential of each muscle using a myoelectric potential sensor provided in the rectus abdominis, the gluteus medius, the femoral stretcher, the anterior tibial muscle, and the like. The myoelectric potential acquisition unit 3 includes, for example, myoelectric potentials of at least four abdominal rectus muscles (RA), gluteus medius muscles (GM), femoral tonic muscles (TFL), and anterior tibial muscles (TA), and two spine uprights. The muscle (ES) and the long thumb flexor muscle (FHL) are acquired. The muscle myoelectric potential acquired by the myoelectric potential acquisition unit 3 is an example, and the present invention is not limited to this.

  As shown in FIG. 2, the myoelectric potential waveforms of the rectus abdominis (RA) and the gluteus medius (GM) are highly correlated. Similarly, the myoelectric potential waveforms of the femoral cord muscle (TFL) and the anterior tibial muscle (TA) are highly correlated. The myoelectric potential acquisition unit 3 acquires the myoelectric potentials of at least the rectus abdominis muscle, the gluteus medius muscle, the femoral stretcher muscle, and the anterior tibial muscle, which have a high correlation, and based on these myoelectric potentials, the muscle synergistic feature described later The amount can be calculated with high accuracy. Therefore, the degree of recovery described later can be evaluated with higher accuracy.

  The myoelectric potential acquisition unit 3 outputs the acquired myoelectric potentials of at least the rectus abdominis muscle, the gluteus medius muscle, the femoral stretcher muscle, and the anterior tibial muscle to the feature amount calculation unit 4.

  The feature amount calculation unit 4 is a specific example of feature amount calculation means. The feature amount calculation unit 4 is based on the myoelectric potential acquired by the myoelectric potential acquisition unit 3, and shows muscle synergistic feature amounts indicating the cooperative state of at least the rectus abdominis, the gluteus medius, the femoral cord muscle, and the anterior tibial muscle Is calculated.

  Muscle synergy refers to, for example, a phenomenon in which when a person performs a certain motion, a plurality of muscles work in cooperation with redundancy. The feature amount calculation unit 4 calculates the later-described feature amounts of SSI and SCI as muscle synergy feature amounts. These feature amounts serve as an index for objectively evaluating the recovery degree (balance ability) of the trainer through rehabilitation training from the viewpoint of the muscle synergy.

Next, a method for calculating the muscle synergy feature SCI will be described.
The myoelectric potential acquisition unit 3 acquires the time series of the myoelectric potentials of the above-described muscles (at least the rectus abdominis muscle, the gluteus medius muscle, the femoral stretcher muscle, and the anterior tibial muscle) 1, 2,. The myoelectric potential acquisition unit 3 is performed, for example, at regular time intervals until the trainee's operation starts and ends (during a balance test). The value of the myoelectric potential at the j-th time point of the i-th muscle is stored in the element M [i, j] in the i-th row and j-th column of the myoelectric potential matrix M. That is,
A horizontal vector M ⁽¹⁾ consisting of a time series of myoelectric potentials of muscle 1,
Horizontal vector M ⁽²⁾ consisting of time series of myoelectric potential of muscle 2
A horizontal vector M ^(m) consisting of a time series of myoelectric potentials of muscle m,
Is the myoelectric potential matrix M.

  Therefore, the number of rows of the myoelectric potential matrix M is m. The number of columns of the myoelectric potential matrix M varies depending on the measurement time length, that is, the operation time length, the measurement frequency during the operation, and the measurement interval.

In this way, the myoelectric potential acquisition unit 3 acquires the myoelectric potential matrix M. The feature quantity calculation unit 4 calculates the muscle synergy matrix W, the control matrix C, and the error matrix E based on the myoelectric potential matrix M acquired by the myoelectric potential acquisition unit 3 so that the following formula is established. In this case, non-negative matrix factorization is used.
M = WC + E

Hereinafter, in order to facilitate understanding, the subscript _k is omitted as appropriate. In non-negative matrix factorization, the degree of error is minimized or the degree of similarity L is maximized.

Here, the number of columns of the myoelectric matrix M, the number of columns of the control matrix C, and the number of columns of the error matrix E are all t, the number of rows of the myoelectric matrix M, the number of rows of the myocardial matrix W, and the error matrix. If the number of rows of E is m, the number of columns of the muscle synergy matrix W and the number of rows of the control matrix C are both n, the similarity L can be defined as follows.
L = 1−1 / m × Σ _{i = 1} ^m √ [Σ _{j = 1} ^t E [i, j] ² ] / √ [Σ _{j = 1} ^t (WC) [i, j] ² ]

  Here, n is a numerical value representing the number of synergies. In general, if n is increased, L is also increased, but an appropriate size of n can be determined as follows by appropriately using non-negative matrix factorization.

  In general, in non-negative matrix factorization, it is desirable to select a synergy number n such that the similarity L is 70% or more. On the other hand, if the number of synergies n is too large, the calculation load increases and over adaptation occurs, which makes it impossible to perform appropriate processing. Therefore, the following method is used. That is, the similarity L is calculated for each of n = 1, 2, 3, 4,.

  For example, as the number of synergies n increases, the degree of similarity L also increases, but the number of synergies n is about 5 and the degree of increase is saturated, and more than 70%. Therefore, numerical values before and after the start of saturation, for example, 4, 5 or 6, can be employed as the synergy number n for performing the subsequent calculations.

  The number n of synergies may be different for each trainer, or it may be a value common to all trainees because there is no significant difference in the number of synergies when the trainer performs a certain action. In the latter case, some trainers are experimentally operated in advance, and an appropriate value of n is determined by non-negative matrix factorization. It is also used as it is for the person.

In this model, when the trainer's central nerve gives n control signals C ⁽¹⁾ , C ⁽²⁾ ,..., C ⁽ⁿ⁾ to m muscles, the muscle 1 has a myoelectric potential WC. ⁽¹⁾ Try to move to satisfy, muscle 2 tries to move to meet myoelectric potential WC ⁽²⁾ , ..., muscle m tries to move to meet myoelectric potential WC ^(m) Assumes that

Here, for example, the control signals C ⁽¹⁾ , C ⁽²⁾ ,..., C ⁽ⁿ⁾ are as independent and non-redundant as possible, that is, the muscle synergy matrix W Unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ do not vary from each other and are gathered together, the better the function of the central nervous system with respect to its movement and the higher the degree of rehabilitation recovery. Therefore, the feature amount calculation unit 4 calculates a feature amount related to the function of the central nervous system and the degree of recovery of rehabilitation from the control matrix C and the muscle synergy matrix W.

Unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ do not vary from each other and are united, which means that the unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ means that the space spread is narrow. That is, the length of each side is 1, and the direction of each side is W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ , the n-dimensional volume of the n-dimensional parallel polyhedron is small. The unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ are the farther from the right angle, the longer the unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ do not vary from each other and are grouped together.

Unit vertical vectors W ⁽¹⁾ , W ⁽²⁾ ,. . . , W ⁽ⁿ⁾ does not vary, and various features can be adopted as the feature amount indicating that they are gathered. For example, it is conceivable to use an inner product of vectors. The inner product p (x, y) for two vectors x and y is expressed as x [1], x [2],. . . , X [u] and each element of the vector y are y [1], y [2],. . . , Y [u], respectively, can be defined as follows.
p (x, y) = Σ _{i = 1} ^u (x [i] × y [i])

Then, the feature value SCI for the muscle synergy matrix W can be determined as follows.
SCI = 2 / [n (n + 2)] × Σ _{i = 1} ⁿ Σ _{j = 1} ⁿ _{, j ≠ i} p (W ⁽ⁱ⁾ , W ^(j) )
The feature amount calculation unit 4 calculates the muscle synergy feature amount SCI using the above formula.

  This muscle synergy feature value SCI varies, for example, between 0 and 1, and becomes a larger numerical value as the recovery degree of rehabilitation is higher.

Next, a method for calculating the muscle synergy feature amount SSI will be described. The muscle synergy feature value SSI is calculated in the same manner as the muscle synergy feature value SCI as follows.
Similar to the above, the myoelectric potential acquisition unit 3 has p operations k = 1, 2,. . . , P for a task (at least rectus abdominis, gluteus medius, femoral tonicus and anterior tibialis) 1, 2,. . . , M, a myoelectric potential matrix M ₁ consisting of a time series of myoelectric potentials during the action of the trainee, and a myoelectric potential matrix M ₂ consisting of a time series of myoelectric potentials during the action 2 of the trainee. . . Then, a myoelectric potential matrix M _p composed of a time series of myoelectric potentials while the trainee performed the operation p is acquired. The electromyograms M ₁ , M ₂ ,. . . , M _p have m rows, but the myoelectric potential matrices M ₁ , M ₂ ,. . . , M _p may be different depending on the length of time for each operation.

Based on the myoelectric potential matrix acquired by the myoelectric potential acquiring unit 3, the feature amount calculating unit 4 calculates the myoelectric synergy matrix W _k , the control matrix C _k , and the error matrix E _k so that the following equations are established.
M _k = W _k C _k + E _k (k = 1, 2,..., P)

Mean e (x) of elements included in vector x, variance v (x) of elements included in vector x, standard deviation s (x) of elements included in vector x, correlation coefficient for two vectors x and y The operation r (x, y) calculates each element of the vector x as x [1], x [2],. . . , X [u] and each element of the vector y are y [1], y [2],. . . , Y [u], respectively, can be defined as follows.
e (x) = (1 / u) × Σ _{i = 1} ^u x [i],
v (x) = (1 / u) × Σ _{i = 1} ^u (x [i] −e (x)) ² ,
s (X) = v (X) ^1/2
r (x, y) = Σ _{i = 1} ^u (x [i] −e (x)) × (y [i] −e (y)) / [m × s (x) × s (y)]

The feature amount calculation unit 4 calculates the muscle synergy feature amount SSI using the following equation.
SSI = 2 / [n × p (p-1)] × Σ i = 1 n Σ k = 1 p Σ h = 1 p, h ≠ k r (W k (i), W h (i))

  Similar to the SCI, the muscle synergy feature amount SSI also changes between 0 and 1, for example, and becomes a larger numerical value as the degree of rehabilitation recovery is higher. In addition, about SCI and SSI mentioned above, it is disclosed by Unexamined-Japanese-Patent No. 2015-73805 which this applicant has already submitted, and this shall be used suitably.

  The display unit 5 is a specific example of display means. The display unit 5 is, for example, a liquid crystal display or an organic EL display. The display unit 5 includes a regression model set in advance and a feature amount calculation unit on a coordinate system (hereinafter referred to as a muscle synergy feature amount / evaluation point coordinate system) in which the muscle synergy feature amount is the horizontal axis and the evaluation point is the vertical axis. The coordinates of the current muscle synergy feature amount (SCI or SSI) calculated by 4 and the current evaluation point acquired by the evaluation point acquisition unit 2 are displayed (FIG. 3).

  The regression model is a line (formula) indicating changes in muscle synergistic features and evaluation point coordinates when the trainee recovers. For example, it is calculated based on the muscle synergy feature quantity (SCI or SSI) calculated by the feature quantity calculation unit 4 and the evaluation score acquired by the evaluation score acquisition unit 2 in the process of recovery of the trainee. . The regression model is calculated using the least squares method (residual sum of squares) or the like based on the calculated muscle synergy feature and the coordinates of the evaluation points. Note that the regression model is preferably calculated based on the muscle synergistic features of the person having the same attributes (gender, age, etc.) as the trainer who evaluates the degree of recovery of rehabilitation and the coordinates of the evaluation points. The regression model calculated as described above is stored in advance in a memory, for example.

  As described above, the coordinates of the current muscle synergy feature amount calculated by the feature amount calculation unit 4 and the current evaluation point acquired by the evaluation point acquisition unit 2 are displayed on the muscle synergy feature amount / evaluation point coordinate system. To do. In this way, the balance ability of the trainer is evaluated in terms of both the muscle synergy feature value and the evaluation point, and by providing redundancy, the muscle synergy feature value is prevented from varying due to daily changes in the physical condition of the trainer. it can. For example, when the trainee is in poor physical condition, not only the muscle synergy feature amount is reduced, but also the evaluation score is simultaneously reduced. Conversely, when the trainee's physical condition is good, not only the muscle synergy feature amount increases but also the evaluation score increases at the same time. In this way, muscle synergy features and evaluation points have a certain correlation, and this correlation is not affected by changes in the physical condition of the trainee's daily condition. Can be expressed as

  In particular, since rehabilitation training is performed over a long period (for example, 6 months to several years), the physical condition of the trainee may change significantly during that time. Therefore, as in the first embodiment, by displaying the current muscle synergy feature amount and the coordinates of the evaluation point on the muscle synergy feature amount / evaluation point coordinate system, the muscle synergy due to the daily physical condition change of the trainee, etc. By effectively suppressing variation in the feature amount, the recovery degree of the trainer can be accurately evaluated over the long-term rehabilitation training.

  Furthermore, in the present embodiment, not only the current muscle synergy feature value and the coordinates of the evaluation point, but also a regression model serving as an index when the trainee is in a recovery state are simultaneously displayed on the muscle synergy feature amount / evaluation point coordinate system. indicate. As a result, the trainer can visually and accurately grasp whether he / she is in a recovery tendency simply by comparing the regression model with the current muscle synergy feature and coordinates of the evaluation point. .

FIG. 4 is a flowchart illustrating an example of a processing flow of the rehabilitation evaluation method according to the first embodiment.
The evaluation score acquisition unit 2 acquires an evaluation score obtained by performing a balance test and evaluating the balance ability of the trainee (step S101).

The myoelectric potential acquisition unit 3 acquires the myoelectric potentials of at least the rectus abdominis muscle, the gluteus medius muscle, the femoral stretcher muscle, and the anterior tibial muscle among the trainee's muscles during the balance test (step S102).
The feature amount calculation unit 4 is based on the myoelectric potential acquired by the myoelectric potential acquisition unit 3, and shows muscle synergistic feature amounts indicating the cooperative state of at least the rectus abdominis, the gluteus medius, the femoral cord muscle, and the anterior tibial muscle Is calculated (step S103).

  The display unit 5 is acquired by the regression model set in advance on the muscle synergy feature / evaluation point coordinate system and the current muscle synergy feature and evaluation point acquisition unit 2 calculated by the feature amount calculation unit 4. The coordinates of the current evaluation point are displayed (step S104).

  As described above, in the rehabilitation evaluation apparatus 1 according to the first embodiment, the myoelectric potential acquisition unit 3 acquires the myoelectric potential of a predetermined muscle among the muscles of the trainee during the balance test. Then, the feature amount calculation unit 4 calculates a muscle synergy feature amount indicating the cooperation state of a predetermined muscle based on the myoelectric potential acquired by the myoelectric potential acquisition unit 3.

Further, the display unit 5 acquires the regression model set in advance on the muscle synergy feature / evaluation point coordinate system and the current muscle synergy feature and evaluation point acquisition unit 2 calculated by the feature amount calculation unit 4. And the coordinates of the current evaluation point. By evaluating the balance ability of the trainer in terms of both the muscle synergy feature value and the evaluation point and providing redundancy, it is possible to suppress variations in the muscle synergy feature value due to changes in the daily physical condition of the trainer. Therefore, the recovery degree of the trainee can be evaluated with higher accuracy. Further, on the muscle synergy feature quantity / evaluation point coordinate system, not only the current muscle synergy feature quantity and the coordinates of the evaluation point, but also a regression model serving as an index of the recovery state is simultaneously displayed. Thus, the trainer can visually and accurately grasp whether or not he / she is in a recovery tendency simply by comparing this regression model with the current muscle synergy feature and coordinates of the evaluation point. .
That is, according to the rehabilitation evaluation apparatus 1 according to the first embodiment, the degree of recovery of the trainee can be accurately evaluated, and whether or not the trainee is in a recovery tendency can be accurately grasped.

Embodiment 2
FIG. 5 is a block diagram illustrating a schematic configuration of the rehabilitation evaluation apparatus 20 according to the second embodiment of the present invention. The rehabilitation evaluation apparatus according to the second embodiment includes an attribute information acquisition unit 6 that acquires attribute information indicating the attributes of the trainee, a target point setting unit 7 that sets at least one target point targeted by the trainee, Is further provided.

  The attribute information acquisition unit 6 is a specific example of attribute information acquisition means. For example, the attribute information acquisition unit 6 acquires attribute information such as the age, sex, height, and weight of the trainee via an input device. The input device is, for example, a keyboard, a mouse, a touch panel, a personal computer, a portable terminal, or the like.

  The target point setting unit 7 is a specific example of target point setting means. The target point setting unit 7 calculates, for example, at least one target point that is a point along the regression model based on the attribute information acquired by the attribute information acquisition unit 6. For example, the target point setting unit 7 sets a high point on the regression model as a target point as the trainee's age becomes younger (lowers) based on the age of the trainee acquired by the attribute information acquisition unit 6. To do. This is because the degree of recovery increases as the trainee's age gets younger.

  The display unit 5 displays the regression model, the current muscle synergy feature amount calculated by the feature amount calculation unit 4, and the current evaluation acquired by the evaluation point acquisition unit 2 on the muscle synergy feature / evaluation point coordinate system. The coordinates of the points and the target points set by the target point setting unit are displayed (FIG. 6). Thereby, the trainer can recognize the target point that he / she is aiming for more clearly while recognizing the current muscle synergy feature value and the coordinates of the evaluation point.

  The target point setting unit 7 is at least one set lower than the final target point between the trainer's current muscle synergy feature and the coordinates of the evaluation point and the final target point (final target point). Two target points (milestone 1, milestone 2,...) May be set. The target point setting unit 7 may set these milestones based on the variation (variance value) of the muscle synergy feature and the evaluation point coordinates. The display unit 5 displays, on the muscle synergy feature / evaluation point coordinate system, the regression model, the coordinates of the current muscle synergy feature and the evaluation point, and the target point and milestone set by the target point setting unit 7. indicate.

  As a result, the trainer does not suddenly aim for the target point, but can first train for the most recent milestone, and feel a sense of accomplishment each time the milestone is reached. In addition, every time a milestone is reached, it is possible to share a sense of accomplishment with related parties and the like through the display on the display unit 5, which leads to motivation to reach the next milestone. In addition, the rehabilitation evaluation apparatus 20 may transmit the screen information displayed on the display unit 5 to another user's mobile terminal (smart phone or the like) via wireless or the like. This makes it possible to share a sense of accomplishment more broadly. In addition, in this Embodiment 2, since another structure is as substantially the same as the said Embodiment 1, the same code | symbol is attached | subjected to the same part and detailed description is abbreviate | omitted.

Embodiment 3
In the third embodiment of the present invention, the coordinates of the muscle synergy feature amount calculated by the feature amount calculation unit 4 and the evaluation point acquired by the evaluation point acquisition unit 2 are sequentially stored in a memory, for example. The memory is a specific example of storage means.

  Based on the past muscle synergy feature values and evaluation points stored in the memory, the display unit 5 displays the locus of changes in the coordinates of the muscle synergy feature values and the evaluation points on the muscle synergy feature value / evaluation point coordinate system. Display (FIG. 7). Thereby, the transition of the trainer's muscle synergy feature and the coordinates of the evaluation points becomes clear. Furthermore, when a target point is set on the muscle synergy feature / evaluation point coordinate system, it is visually determined whether the rehabilitation training of the current trainer is appropriate by seeing how the locus approaches the target point. Can grasp.

  The display unit 5 displays the muscle synergy feature amount and the evaluation point coordinate trajectory for the predetermined period based on the muscle synergy feature amount and the evaluation point for the predetermined period specified by the user. You may display on an evaluation point coordinate system. In addition, in this Embodiment 3, since another structure is substantially the same as the said Embodiment 1, the same code | symbol is attached | subjected to the same part and detailed description is abbreviate | omitted.

Embodiment 4
FIG. 8 is a block diagram showing a schematic configuration of a rehabilitation evaluation apparatus according to Embodiment 4 of the present invention. The rehabilitation evaluation apparatus 40 according to the fourth embodiment includes a coordinate estimation unit 8 that estimates future muscle synergy feature values and evaluation point coordinates based on past muscle synergy feature values and evaluation point coordinates stored in the memory. Is further provided. The coordinate estimation unit 8 is a specific example of estimation means.

  The coordinate estimation unit 8 uses, for example, a time series analysis method or the like based on the past muscle synergy feature value and evaluation point coordinates stored in the memory, and a future muscle synergy feature value and evaluation point after a predetermined time. Guess the coordinates. The display unit 5 has a coordinate estimation unit on the muscle synergy feature / evaluation point coordinate system together with, for example, the locus of changes in the coordinates of the muscle synergy feature and the evaluation point, and the coordinates of the current muscle synergy feature and the evaluation point. The future muscle synergy feature amount estimated by 8 and the coordinates of the evaluation point are displayed (FIG. 9).

  Thus, the trainer can clearly recognize the direction of his / her recovery degree (deterioration, stagnation, good, etc.). For example, when a target point or a milestone is set before the target point, the trainee can visually grasp whether the target point or the milestone can be achieved.

Furthermore, the coordinate estimation unit 8 is based on at least one of the time taken for the unit change amount of the past muscle synergy feature amount stored in the memory and the time taken for the unit change amount of the past evaluation point. Future muscle synergy feature values and coordinates of evaluation points may be estimated with higher accuracy.
. The coordinate estimation unit 8, for example, based on the time taken for the muscle synergy feature value and the coordinates of the evaluation point to change from point a to point b (unit change amount), The coordinates of the evaluation point are estimated (FIG. 10).

  For example, a trainer who takes one week to change the muscle synergy feature value and the coordinates of the evaluation point from point a to point b can be predicted to have a greater change thereafter than a trainer who takes two weeks. Therefore, by considering the time taken for such past muscle synergy feature values or unit change amounts of the evaluation points, it is possible to estimate the future muscle synergy feature values and the coordinates of the evaluation points with higher accuracy. In addition, in this Embodiment 4, since another structure is as substantially the same as the said Embodiment 1, the same code | symbol is attached | subjected to the same part and detailed description is abbreviate | omitted.

Embodiment 5
FIG. 11 is a block diagram showing a schematic configuration of a rehabilitation evaluation apparatus according to Embodiment 5 of the present invention. The rehabilitation evaluation apparatus 50 according to the fifth embodiment further includes a regression model update unit 9 that updates the regression model based on the muscle synergy feature amount and the coordinates of the evaluation points stored in the memory. The regression model update unit 9 is a specific example of an update unit. For example, the regression model update unit 9 reads the regression model set in the memory when the muscle synergy feature amount of the memory and the coordinate data amount of the evaluation point reach a preset data amount, or at a preset predetermined period. Update. Thereby, the latest regression model can be displayed automatically.

Further, the muscle synergy feature value and the coordinates of the evaluation points stored in the memory are the attributes of the trainee, the elapsed time from the start of the trainer's training, the trainer's training content (training method, training amount, etc.), or the trainer May be classified for each degree of recovery (muscle synergy feature amount, recovery level of evaluation points per unit time, etc.). The rehabilitation evaluation apparatus 50 according to the fifth embodiment, based on the classified muscle synergy feature amount and the coordinates of the evaluation points, the attributes of the trainer, the elapsed time from the training start of the trainer, the training content of the trainer, or Further, it has a regression model generation unit 10 that generates a regression model for each recovery degree of the trainee. The regression model generation unit 10 is a specific example of a regression model generation unit.
.

  The display unit 5 displays the regression model corresponding to the trainee generated by the regression model generation unit 10. The display unit 5 includes, for example, the trainee's conditions (trainer attributes, training among the regression models generated by the regression model generation unit 10 based on the trainee information input via an input device or the like. The regression model generated under conditions close to the trainee's training start time (elapsed time, trainer training content, trainer recovery degree, etc.) is selected and displayed. Thereby, since the regression model more suitable for the trainee can be displayed, the manager or the like can design more appropriate training content by the trainee with reference to the regression model. In addition, in this Embodiment 5, since another structure is substantially the same as the said Embodiment 1, the same code | symbol is attached | subjected to the same part and detailed description is abbreviate | omitted.

Note that the present invention is not limited to the above-described embodiment, and can be changed as appropriate without departing from the spirit of the present invention.
In the above-described embodiment, the display unit 5 displays the regression model, the muscle synergy feature amount, the coordinates of the evaluation point, and the like on a coordinate system in which the muscle synergy feature amount is the horizontal axis and the evaluation point is the vertical axis. However, the present invention is not limited to this. For example, the display unit 5 may display the regression model, the muscle synergy feature amount, the coordinates of the evaluation point, and the like on a coordinate system having the muscle synergy feature amount as the vertical axis and the evaluation point as the horizontal axis.

  For example, the present invention can be realized by causing the CPU or a GPU (Graphics Processing Unit) to execute a computer program as shown in FIG.

  The program may be stored using various types of non-transitory computer readable media and supplied to a computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (for example, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (for example, magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / W and semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (random access memory)) are included.

  The program may be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

  DESCRIPTION OF SYMBOLS 1 Rehabilitation evaluation apparatus, 2 evaluation point acquisition part, 3 myoelectric potential acquisition part, 4 feature-value calculation part, 5 display part, 6 attribute information acquisition part, 7 target point setting part, 8 coordinate estimation part, 9 regression model update part, 10 Regression model generator

Claims (9)

A rehabilitation evaluation apparatus for evaluating a recovery degree of a trainer by rehabilitation training,
An evaluation score acquisition means for acquiring an evaluation score obtained by performing a balance test and evaluating the balance ability of the trainee;
At the time of the balance test, a myoelectric potential acquisition means for acquiring a myoelectric potential of a predetermined muscle among the trainee's muscles;
Feature amount calculating means for calculating a muscle synergy feature amount indicating a cooperative state of the predetermined muscle based on the myoelectric potential acquired by the myoelectric potential acquiring means;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, the muscle synergy feature amount calculated by the feature amount calculation unit, and the Display means for displaying the coordinates of the evaluation points acquired by the evaluation point acquisition means;
A rehabilitation evaluation apparatus comprising: The rehabilitation evaluation apparatus according to claim 1,
Attribute information acquisition means for acquiring attribute information indicating the attributes of the trainee;
Based on the attribute information acquired by the attribute information acquisition means, a target point setting means for setting at least one target point that is a point along the regression model and targeted by the trainee;
With
The display means has, on the coordinate system, the regression model, the muscle synergy feature quantity calculated by the feature quantity calculation means, the coordinates of the evaluation points acquired by the evaluation point acquisition means, and the target point setting. Display the target point set by means,
A rehabilitation evaluation apparatus characterized by that. The rehabilitation evaluation apparatus according to claim 1 or 2,
A storage means for storing the coordinates of the muscle synergy feature quantity calculated by the feature quantity calculation means and the evaluation point acquired by the evaluation point acquisition means;
The display means displays, on the coordinate system, the locus of changes in the coordinates of the muscle synergy feature value and the evaluation point based on the past muscle synergy feature value and the evaluation point stored by the storage means.
A rehabilitation evaluation apparatus characterized by that. The rehabilitation evaluation apparatus according to claim 3,
Based on the past muscle synergy feature and the coordinates of the evaluation points stored by the storage means, the apparatus further comprises an estimation means for estimating the future muscle synergy feature and the coordinates of the evaluation points,
The display means includes, on the coordinate system, a future change of muscle synergy estimated by the estimation means together with a locus of changes in the coordinates of the muscle synergy feature and evaluation points and a current muscle synergy feature and coordinates of the evaluation points. Display the feature amount and the coordinates of the evaluation point.
A rehabilitation evaluation apparatus characterized by that. The rehabilitation evaluation apparatus according to claim 4,
The estimation means is based on at least one of a time taken for a unit change amount of a past muscle synergy feature quantity stored by the storage means and a time taken for a unit change quantity of a past evaluation point, based on the future. Estimate the muscle synergy features and evaluation point coordinates of
A rehabilitation evaluation apparatus characterized by that. The rehabilitation evaluation apparatus according to any one of claims 3 to 5,
Update means for updating the regression model based on the muscle synergy feature quantity and the coordinates of the evaluation points stored by the storage means;
The display means displays the regression model updated by the update means;
A rehabilitation evaluation apparatus characterized by that. The rehabilitation evaluation apparatus according to claim 6,
The muscle synergy feature value and the coordinates of the evaluation points stored by the storage means are the attributes of the trainee, the elapsed time from the training start of the trainer, the training content of the trainer, or the recovery degree of the trainer, It is classified by each,
Based on the classified muscle synergy feature and the coordinates of the evaluation points, the attributes of the trainer, the elapsed time from the start of training of the trainer, the training content of the trainer, or the recovery degree of the trainer, A regression model generating means for generating a regression model of
The display means displays a regression model corresponding to the trainee generated by the regression model generation means.
Rehabilitation evaluation device characterized by this. A rehabilitation evaluation method for evaluating a recovery degree of a trainer by rehabilitation training,
Performing a balance test to obtain an evaluation score that evaluates the balance ability of the trainee;
Obtaining a myoelectric potential of a predetermined muscle among the muscles of the trainer during the balance test; and
Calculating a muscle synergy feature amount indicating a cooperation state of the predetermined muscle based on the acquired myoelectric potential;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, and the calculated muscle synergy feature amount and the coordinate of the evaluation point, Steps to display;
The rehabilitation evaluation method characterized by including. A program for assessing the recovery level of trainees through rehabilitation training,
A process of performing a balance test to obtain an evaluation score that evaluates the balance ability of the trainee;
A process of calculating a muscle synergy feature amount indicating a cooperation state of the predetermined muscle based on a myoelectric potential of the predetermined muscle among the muscles of the trainee acquired at the balance test;
On the coordinate system, a regression model indicating changes in the coordinates of the muscle synergy feature amount and the evaluation point when a preset trainer recovers, and the calculated muscle synergy feature amount and the coordinate of the evaluation point, Processing to display,
A program characterized by causing a computer to execute.

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