Walking evaluation score calculation device, method and program

Abstract

To provide a device, a method, and a program which can easily and inexpensively calculate various useful evaluation scores based on a joint angle of a subject during walking.SOLUTION: A walking evaluation score calculation device includes: an acceleration data acquisition unit 110; a joint angle data calculation unit 140 which calculates right and left joint angle data 173 in one walking cycle from the acquired acceleration data; a joint score calculation unit 151 which calculates right and left joint scores 174 in the one walking cycle on the basis of the calculated right and left joint angle data 173 in the one walking cycle and reference data 178; and an evaluation score calculation processing unit 152 which calculates a walking evaluation score 175 on the basis of the calculated right and left joint scores 174.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technique for calculating a walking evaluation score. [Background technology]

[0002] Various methods have been proposed for observing the walking behavior of pedestrians and calculating quantitative walking evaluation scores based on the observation results. For example, in the method described in Patent Document 1, a pressure-sensitive sheet is placed on a walkway to observe the footprints of pedestrians and calculate various scores based on the observation results. In another method described in Patent Document 2, a depth camera is used to observe the movement trajectory of a specific part of a pedestrian, such as the head, and various scores are calculated based on the observation results. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-052999 A [Patent Document 2] JP 2017-205134 A [Patent Document 3] Patent No. 6111837 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the method described in Patent Document 1 requires a pressure-sensitive sheet, which is costly and requires time and space to lay the pressure-sensitive sheet. The method described in Patent Document 2 also requires an expensive depth camera, which is costly. The method described in Patent Document 2 also requires a photographer in addition to the subject, or a tripod or the like is required if a photographer cannot be prepared, which requires a large space and time for measurement.

[0005] As a solution to this problem, a technology has been proposed that calculates a walking score based on data detected by an acceleration sensor worn by a subject (see Patent Document 3). Acceleration sensors are inexpensive and are often built into mobile devices such as smartphones, which have become widespread in recent years. In addition, since the acceleration sensor is worn by the subject to perform measurements, no space is required for photography. This solves the problems in Patent Documents 1 and 2 mentioned above.

[0006] Incidentally, changes in the joint angles of a walker while walking have been considered as an analysis target for evaluating the gait of a walker. However, the method described in Patent Document 3 calculates the "deviation of the center of gravity position" of the subject, and is unable to detect the joint angles while walking. On the other hand, the method described in Patent Document 2 above proposes estimating the joint angles, but as mentioned above, there are problems such as high cost. Furthermore, although the method described in Patent Document 2 proposes estimating the joint angles, it does not propose a useful and quantitative evaluation score from the joint angles as an evaluation index.

[0007] The present invention has been made in consideration of the above-mentioned circumstances, and an object of the present invention is to provide an apparatus, method, and program that can easily and inexpensively calculate various useful evaluation scores based on the joint angles of a subject while walking. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, the walking evaluation score calculation device of the present invention is characterized by comprising an acceleration data acquisition unit that acquires acceleration data of a subject while walking, measured by an acceleration sensor fixed to the subject; a joint angle data calculation unit that calculates right side joint angle data, which is joint angle data of the right half of the body in one walking cycle, and left side joint angle data, which is joint angle data of the left half of the body in one walking cycle, based on the acquired acceleration data; a joint score calculation unit that calculates a right side joint score in one walking cycle based on the calculated right side joint angle data in one walking cycle and its reference data, and calculates a left side joint score in one walking cycle based on the calculated left side joint angle data in one walking cycle and its reference data; and a walking evaluation score calculation unit that calculates a walking evaluation score based on the calculated right side joint score and left side joint score.

[0009] As an example of a preferred aspect of the present invention, the walking evaluation score calculation unit includes a left-right difference evaluation score calculation unit that calculates a left-right difference evaluation score, which is the average value of the difference between the right joint score and the left joint score within one walking cycle.

[0010] In addition, as an example of another preferred aspect of the present invention, the walking evaluation score calculation unit includes a left / right average evaluation score calculation unit that calculates a left / right average evaluation score, which is the average value of the right joint score and the left joint score within one walking cycle.

[0011] In addition, as an example of a preferred aspect of the present invention, the walking evaluation score calculation unit includes a muscle force exertion period memory unit which stores a muscle force exertion period, which is a period during which a muscle that affects walking exerts force within one walking cycle, and a muscle strength evaluation score calculation unit which calculates a muscle strength evaluation score, which is the average value of the right joint score or the left joint score for a joint associated with a muscle within the muscle force exertion period. Effect of the Invention

[0012] According to the present invention, a gait evaluation score is calculated based on acceleration data of a subject while he / she is walking. This acceleration data can be easily and inexpensively obtained by an acceleration sensor. Therefore, the gait evaluation score of the subject can be analyzed simply and inexpensively. In addition, in the present invention, right and left joint scores are calculated based on right and left joint angle data calculated from acceleration data and its reference data, and a gait evaluation score is calculated from these joint scores, so that various quantitative and useful evaluation scores based on the index of reference data can be obtained. [Brief description of the drawings]

[0013] [Figure 1] Functional block diagram of the exercise support device [Diagram 2] 1 is a flowchart illustrating the operation of the exercise support device. [Diagram 3] An example of measured acceleration data [Figure 4] An example of total acceleration data [Diagram 5] Diagram explaining heel contact timing [Figure 6] A diagram explaining the division of the first walking cycle into odd-numbered phases and the second walking cycle into even-numbered phases. [Figure 7] FIG. 1 is a diagram for explaining standardization and averaging of acceleration data; [Figure 8] A diagram explaining left / right determination during walking cycle [Figure 9] An example of averaged acceleration data [Figure 10] An example of averaged joint angle data [Figure 11] FIG. 1 is a diagram for explaining a calculation process of joint angle data. [Figure 12] FIG. 1 is a diagram for explaining a process for calculating joint scores. [Figure 13] Data structure of walking assessment scores [Figure 14] A diagram explaining the relationship between the sagittal plane angle of the hip joint and muscle movement during one gait cycle. [Figure 15] A diagram explaining the relationship between the sagittal plane angle of the knee joint and muscle movement during one gait cycle. [Figure 16]A diagram explaining the relationship between the sagittal plane angle of the ankle joint and muscle movement during one gait cycle. [Figure 17] Diagram explaining the period of exertion of muscle force in one direction cycle [Figure 18] Flowchart for explaining the process of presenting an exercise menu [Figure 19] Flowchart for explaining the process of presenting an exercise menu [Figure 20] Flowchart for explaining the process of presenting an exercise menu DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] An exercise support device according to an embodiment of the present invention will be described with reference to the drawings. The exercise support device according to this embodiment acquires acceleration data during walking from an acceleration sensor attached to a subject, calculates various walking evaluation scores from this acceleration data, and presents an exercise menu to the subject based on this walking evaluation score. Therefore, the exercise support device according to this embodiment includes a calculation device for the walking evaluation score. Figure 1 is a functional block diagram of the exercise support device, and Figure 2 is a flowchart explaining the operation of the exercise support device.

[0015] The exercise support device 100 according to the present embodiment is carried by the subject and acquires acceleration data of the subject while walking. The exercise support device 100 calculates right and left joint angle data in one walking cycle of the subject based on the acquired acceleration data, and further calculates a walking evaluation score based on the calculated right and left joint angle data. Furthermore, the exercise support device 100 presents an exercise menu to the subject using the calculated walking evaluation score. Details of the exercise support device 100 will be described below.

[0016] 1, exercise support device 100 includes acceleration data acquisition unit 110, data range setting unit 120, walking cycle determination unit 130, joint angle data calculation unit 140, walking evaluation score calculation unit 150, exercise menu presentation unit 160, and storage unit 170. Exercise support device 100 is also connected to acceleration sensor 200 and display device 300. Note that the exercise support device 100 excluding exercise menu presentation unit 160 corresponds to a walking evaluation score calculation device.

[0017] Each unit of the exercise support device 100 can be configured by a conventionally known computer equipped with a main processing unit, a main memory unit, an auxiliary memory unit, an input device, etc. The exercise support device 100 can be implemented by installing a program that causes each unit described above to function in a computer. The exercise support device 100 can be implemented as dedicated hardware. The exercise support device 100 can be distributed and implemented in a plurality of devices. In this embodiment, the exercise support device 100 is implemented by installing a program in a terminal that is a high-function mobile communication terminal called a "smartphone" and has an acceleration sensor 200 and a display device 300 built in.

[0018] The acceleration sensor 200 is made up of a well-known three-axis inertial sensor, and detects the acceleration of a pedestrian while walking. The accelerations detected here are the acceleration in the vertical direction (Z-axis direction), the acceleration in the walking direction (Y-axis direction), and the acceleration in the left and right horizontal directions (X-axis direction) relative to the walking direction. In this embodiment, the right direction of the X-axis is positive and the left direction is negative relative to the walking direction.

[0019] The acceleration data acquisition unit 110 acquires acceleration data of the walking subject measured by the acceleration sensor 200 at a predetermined measurement rate (for example, 100 Hz) (step S1 in FIG. 2). Here, during the measurement of acceleration, the relative position of the acceleration sensor 200 with respect to the subject, i.e., the relative position of the exercise support device 100, is fixed so that each axial direction of the acceleration sensor 200 is constant. The fixed position of the exercise support device 100 is preferably closer to the subject's midline in the left-right direction. Moreover, the fixed position of the exercise support device 100 is preferably higher than above the crotch in the vertical direction, and is preferably lower than the neck. Therefore, during the measurement of acceleration, it is preferable to fix the exercise support device 100 around the navel in the center of the abdomen of the subject or around the center of the waist on the back side in the left-right direction. The acceleration data acquisition unit 110 stores the acquired acceleration data 171 in the storage unit 170. An example of the acquired acceleration data 171 is shown in FIG. 3.

[0020] The data range setting unit 120 sets a time range to be analyzed from the acceleration data 171. This is because the acceleration data acquisition unit 110 sets the time range so that only the acceleration data related to stable walking is analyzed, since the period from the start of measurement to the end of measurement includes a period during which the user is not walking, and walking is not stable immediately after starting walking or immediately before stopping walking.

[0021] The data range setting unit 120 adds the accelerations of the X-axis, Y-axis, and Z-axis of the acceleration data 171, and applies a low-pass filter to the added acceleration data to remove noise (sine wave processing) to generate combined acceleration data (step S2 in FIG. 2). The data range setting unit 120 sets the time range to be analyzed based on this combined acceleration data (step S3 in FIG. 2). FIG. 4 shows an example of combined acceleration data. In FIG. 4, the time when the combined acceleration reaches a peak value (maximum value) equal to or greater than a predetermined value is the time of heel contact, and the period from when the first peak value is detected to when the last peak value is detected is considered to be the walking time range.

[0022] The data range setting unit 120 may set the time range automatically using a predetermined algorithm, or may set the time range based on an input instruction from the user. In the former case, for example, as shown in Fig. 4, the data range setting unit 120 detects the time when the resultant acceleration reaches a peak value (maximum value) equal to or greater than a predetermined value as the time of heel strike, and sets the time range to be analyzed from the second heel strike to the penultimate heel strike. In the latter case, the data range setting unit 120 displays the resultant acceleration data on the display device 300, accepts input of the time range to be analyzed from the user, and sets this as the time range to be analyzed.

[0023] The walking cycle determination unit 130 divides the acceleration data 171 into a first walking cycle, which is a walking cycle from when one foot of the subject touches the ground to when the other foot touches the ground next, and a second walking cycle, which is a walking cycle from when the other foot of the subject touches the ground to when the other foot touches the ground next. Furthermore, the walking cycle determination unit 130 determines whether the first walking cycle and the second walking cycle are walking cycles related to the right foot or the left foot, respectively. The walking cycle determination unit 130 will be described in detail below.

[0024] The walking cycle determination unit 130 refers to the above-mentioned total acceleration data within the time range set by the data range setting unit 120, detects and counts the time when the resultant acceleration reaches a peak value (maximum value) as the time of heel strike, and stores it as heel strike timing information. Figure 5 shows the relationship between the resultant acceleration and the heel strike timing.

[0025] Next, the walking cycle determination unit 130 refers to the heel strike timing information and divides the acceleration data 171 into a plurality of sections between adjacent odd count values ​​(called "odd phases") and a plurality of sections between adjacent even count values ​​(called "even phases") (step S4 in FIG. 2). Each odd phase is a first walking cycle that is a walking cycle from when one heel of the subject touches the ground to when the other heel touches the ground next. Specifically, the odd phases are from the first heel strike to the third heel strike, from the third heel strike to the fifth heel strike, ... from the 2n+1 heel strike to the 2n+3 heel strike. Similarly, each even phase is a second walking cycle that is a walking cycle from when the other heel of the subject touches the ground to when the other heel touches the ground next. Specifically, the even phases are from the second heel strike to the fourth heel strike, from the fourth heel strike to the sixth heel strike, ... from the 2n heel strike to the 2n+2 heel strike. Figure 6 shows the first gait cycle with the divided odd phases and the second gait cycle with the divided even phases.

[0026] Next, the walking cycle determination unit 130 normalizes the unit of time axis direction for each walking cycle of the divided odd phase and even phase from real time to 0 to 1 (0 to 100%) (step S5 in FIG. 2). In the present invention, the joint angle is analyzed based on the waveform characteristics of the acceleration in each walking cycle, but since the walking of the subject is not stable, the periodic time of each walking cycle varies. For this reason, in the present invention, the normalization process in the time axis direction is performed as described above.

[0027] Next, the walking cycle determination unit 130 refers to the acceleration data 171 as shown in Fig. 7, calculates the average of the accelerations of the X-axis, Y-axis, and Z-axis in each first walking cycle for the odd-numbered phase, calculates the average acceleration data 172 of each of the X-axis, Y-axis, and Z-axis in one walking cycle, and stores this in the storage unit 170 (step S6 in Fig. 2). The walking cycle determination unit 130 similarly calculates the average acceleration data 172 in the even-numbered phase, and stores this in the storage unit 170. As a result of the left / right determination of each phase by the process described later, the two average acceleration data 172 stored in the storage unit 170 are treated as the average acceleration data 172a related to the right foot and the average acceleration data 172b related to the left foot as shown in Fig. 1.

[0028] Next, the walking cycle determination unit 130 determines whether the first walking cycle in the odd phase is a walking cycle related to the right leg or the left leg based on the waveform characteristics of the averaged acceleration on the X-axis of either the odd phase or the even phase (here, the odd phase) (step S7 in FIG. 2).

[0029] Specifically, as shown in FIG. 8, the walking cycle determination unit 130 performs left / right determination based on the value of the average acceleration of the X-axis in the first half of the time axis direction of the walking cycle, preferably in a time range of 20 to 50%. More specifically, in the time range, the number of positive average accelerations of the X-axis and the number of negative average accelerations of the X-axis are counted and compared. Then, when the number of positive average accelerations of the X-axis is greater, it is determined that the first walking cycle of the odd phase is a walking cycle related to the left foot. On the other hand, when the number of negative average accelerations of the X-axis is greater, it is determined that the first walking cycle of the odd phase is a walking cycle related to the right foot. Hereinafter, a walking cycle determined by the walking cycle determination unit 130 to be a walking cycle related to the right foot is referred to as a right walking cycle. Similarly, a walking cycle determined by the walking cycle determination unit 130 to be a walking cycle related to the left foot is referred to as a left walking cycle.

[0030] In another embodiment, instead of the positive and negative number of the averaged acceleration on the X-axis, other statistical values ​​such as the integral value of the averaged acceleration on the X-axis can be used as a criterion for left / right determination. In another embodiment, the range in the time axis direction to be determined can be the latter half of the range in the time axis direction instead of the first half of the range. In this case, it is determined that the positive and negative of the averaged acceleration on the X-axis is reversed at the time of determination. In another embodiment, it is possible to perform left / right determination based on a plurality of second walking cycles in the even phase. In this case, it is determined that the positive and negative of the averaged acceleration on the X-axis is reversed at the time of determination.

[0031] The joint angle data calculation unit 140 calculates right side joint angle data 173a, which is angle data of the joints of the right half of the body in a right walking cycle, from right averaged acceleration data 172a in the right walking cycle, and calculates left side joint angle data 173b, which is angle data of the joints of the left half of the body in the left walking cycle, from left averaged acceleration data 172b in the left walking cycle, using a multiple regression model calculated in advance. First, the multiple regression model used in this calculation process will be described in detail.

[0032] This multiple regression model is calculated in advance using another computer prior to the calculation process by exercise support device 100. Parameters of the calculated multiple regression model are stored in advance in storage unit 170 as analysis parameters 177.

[0033] This multiple regression model is calculated by performing principal component analysis and multiple regression analysis using samples of acceleration data (acceleration data for model generation) during walking of multiple subjects measured in advance and joint angle data obtained by imaging and image analysis at the time of the measurement corresponding to the acceleration data for model generation. Here, the acceleration data for model generation and joint angle data used in the calculation of the multiple regression model include both data related to right and left walking cycles. In other words, the multiple regression model is not differentiated for right and left feet, but is a single model common to both right and left feet.

[0034] The joint angle data represents angles between bones that constitute joints of the human body. The joint angle data includes joint angle data related to the right half of the body (right joint angle data) and joint angle data related to the left half of the body (left joint angle data). The right joint angle data corresponds to acceleration data in a right walking cycle. The left joint angle data corresponds to acceleration data in a left walking cycle. In this embodiment, the subject's lower body, i.e., joints below the waist, are the subject. Specifically, the left and right pelvis, hip joints, knee joints, and ankle joints are the subject. Each joint angle data includes an angle in the sagittal plane (angle seen from the side), an angle in the frontal plane (angle seen from the front), and an angle in the horizontal plane (angle seen from above). Therefore, the left and right joint angle data each include 4 locations x 3 directions = 12 measurement values ​​per measurement timing.

[0035] The time range to be analyzed for the model generation acceleration data and the corresponding joint angle data used to create the multiple regression model is set in advance by the same process as that performed by the data range setting unit 120 described above.

[0036] Furthermore, by the same process as that performed by the walking cycle determination unit 130, the acceleration data for generating the model and the corresponding joint angle data are divided into a plurality of right walking cycles and left walking cycles, and normalized in the time axis direction.

[0037] Furthermore, the acceleration data for model generation and the corresponding joint angle data are averaged by the same process as that of the walking cycle determination unit 130. Specifically, the arithmetic average of the accelerations of the X-axis, Y-axis, and Z-axis in each of a plurality of right walking cycles is calculated to calculate the averaged acceleration data of each of the X-axis, Y-axis, and Z-axis in one right walking cycle. The same is true for the left walking cycle. The arithmetic average of the right joint angle data in each direction of the pelvis (sagittal plane, frontal plane, horizontal plane) in each of a plurality of right walking cycles is also calculated to calculate the averaged acceleration data of each direction in one right walking cycle. The same is true for the right joint angle data of the hip joint, knee joint, and ankle joint. Furthermore, the same process is also performed on the left joint angle data.

[0038] The explanatory variable (independent variable) A of the multiple regression model is the principal component [A1, A2, …, A N ] T Here, the number of samples of the observation variables subject to the principal component analysis is two per one walk (a series of movements from the start of walking to the end of walking): averaged acceleration data pertaining to the right walking cycle and averaged acceleration data pertaining to the left walking cycle. Therefore, the number of samples of the observation variables subject to the principal component analysis is the number of walks pertaining to the measurements for generating the model x 2.

[0039] The observation variables to be subjected to the principal component analysis are a data group consisting of averaged acceleration data of multiple axes at each measurement time within one walking cycle. FIG. 9 shows an example of the averaged acceleration data. As shown in FIG. 9, one piece of averaged acceleration data consists of averaged acceleration data of three axes (X-axis, Y-axis, and Z-axis). That is, the entire graph in FIG. 9 is one observation variable. For example, when using the measured values ​​of averaged acceleration data at 1% intervals in the time axis direction within a walking cycle, the number of components of the observation variables is 101 (0% to 100%) x 3 (X-axis, Y-axis, and Z-axis). In the present invention, the method of principal component analysis is used to convert these many components into N principal components.

[0040] Specifically, first, (1) calculate the non-standardized principal component scores of the averaged acceleration data and the eigenvectors of each principal component axis (hereinafter referred to as "acceleration eigenvectors"). Here, for the non-standardized principal component scores to be calculated, N eigenvalues ​​of each principal component axis obtained by the principal component analysis, each of which has an eigenvalue of 1 or more, are selected. (2) Next, standardization processing (average 0, variance 1) is performed on the non-standardized principal component scores of the averaged acceleration data calculated in (1) above to obtain standardized principal component scores A1, A2, ..., A1 of the averaged acceleration data. N The acceleration eigenvectors of the principal component axes calculated in (1) above are stored in the storage unit 170 as one of the analysis parameters 177.

[0041] The dependent variable B of the multiple regression model is the M-dimensional (M: natural number) principal components [B1, B2, …, BM ] T Here, since the averaged joint angle data corresponds to the averaged acceleration data, the number of samples of the observation variables that are the subject of the principal component analysis is the same as the number of samples in the principal component analysis of the averaged acceleration data.

[0042] The observation variables to be subjected to the principal component analysis are a data group consisting of averaged joint angle data of each joint as viewed from a plurality of directions for the plurality of joints at each measurement time within one walking cycle. FIG. 10 shows an example of the averaged joint angle data. As shown in FIG. 10, one averaged joint angle data is made up of averaged joint angle data for a plurality of joints. The averaged joint angle data for each joint is made up of averaged joint angle data for a plurality of directions. That is, the entirety of the plurality of graphs in FIG. 10 becomes one observation variable. For example, when using the measured values ​​of the averaged joint angle data at 1% intervals in the time axis direction within the walking cycle, the number of components of each observation variable is 101 (0% to 100%) x 3 (sagittal plane, frontal plane, horizontal plane) x 4 (pelvis, hip joint, knee joint, ankle joint). In the present invention, the method of principal component analysis is used to convert these many components into M principal components.

[0043] Specifically, first, (1) calculate the unstandardized principal component scores of the averaged joint angle data and the eigenvectors of each principal component axis (hereinafter referred to as "joint angle eigenvectors"). Here, M unstandardized principal component scores with eigenvalues ​​of 1 or more for each principal component axis obtained by the principal component analysis are selected as the calculated unstandardized principal component scores. (2) Next, standardization processing (average 0, variance 1) is performed on the unstandardized principal component scores of the averaged joint angle data calculated in (1) above to obtain standardized principal component scores B1, B2, ..., B M The joint angle eigenvector of each principal component axis calculated in (1) above is stored in the storage unit 170 as one of the analysis parameters 177.

[0044] A multiple regression model is created by performing multiple regression analysis using the explanatory variable (independent variable) A and the response variable (dependent variable) obtained by the above process. The multiple regression model can be expressed by the following formula.

[0045] B1=α 11 ·A1+α 21 ·A2+α 31 ·A3+ … +α N1 A N +β1 B2=α 12 ·A1+α 22 ·A2+α 32 ·A3+ … +α N2 A N +β2 B3=α 13 ·A1+α 23 ·A2+α 33 ·A3+ … +α N3 A N +β3 … B M = α 1M ·A1+α 2M ·A2+α 3M ·A3+ … +α NM A N +β M

[0046] The above formula can be expressed as a vector as follows: B = αA + β

[0047] Here, α is an N×M-dimensional coefficient parameter, and β is an M-dimensional coefficient parameter. These α and β are stored in the storage unit 170 as one of the analysis parameters 177.

[0048] 11, the joint angle data calculation unit 140 calculates right joint angle data 173a from the right average acceleration data 172a, and calculates left joint angle data 173b from the left average acceleration data 172b. Details of the calculation process of the joint angle data in the joint angle data calculation unit 140 will be described below. This process is the same for the left and right, so the calculation process for the right side will be described here as an example.

[0049] First, the joint angle data calculation unit 140 converts the right foot averaged acceleration data 172a stored in the storage unit 170 into N principal components using a principal component analysis technique (step S8 in FIG. 2). Specifically, the right averaged acceleration data 172a of each axis is multiplied by the acceleration eigenvector included in the analysis parameters 177 to calculate a non-standardized principal component score of the right averaged acceleration data, and further performs standardization (average 0, variance 1) processing to calculate a standardized principal component score of the right averaged acceleration data. This standardized principal component score of the right averaged acceleration data is the explanatory variable (independent variable) A of a multiple regression model consisting of N-dimensional principal components.

[0050] Next, the joint angle data calculation unit 140 uses the coefficient parameters of the multiple regression model included in the analysis parameters 177, i.e., the above-mentioned multiple regression model, to calculate the standardized principal component scores of the joint angle data, which is the objective variable (dependent variable) B, from the explanatory variable (independent variable) A (step S9 in FIG. 2). Here, the objective variable (dependent variable) B is M-dimensional data.

[0051] Next, the joint angle data calculation unit 140 uses the standardized principal component scores of the calculated M pieces of joint angle data as principal components and inversely converts the principal components into joint angle data using a method of principal component analysis (step S10 in FIG. 2). Specifically, the joint angle data calculation unit 140 calculates the joint angle data of each joint (pelvis, hip joint, knee joint, ankle joint) of the right half of the body in one right walking cycle in each direction (sagittal plane, frontal plane, horizontal plane) using the standardized principal component scores of the calculated joint angle data and the joint angle eigenvector included in the analysis parameters 177. That is, the right joint angle data 173a is calculated for each combination of (joint, direction). In this embodiment, the right joint angle data 173a is a group of 12 joint angle data (4 joints x 3 directions). Each joint angle data has 101 values ​​at 1% intervals in the time axis direction in one walking cycle. The joint angle data calculation unit 140 stores the calculated joint angle data of the right half of the body in the storage unit 170 as right side joint angle data 173a.

[0052] The joint angle data calculation unit 140 performs a similar analysis process to calculate left side joint angle data 173b based on averaged acceleration data 172b related to the left foot, and stores the data in the storage unit 170. Note that the analysis parameters 177 used in this analysis process are common to the left and right.

[0053] Through the above processing, the joint angle data calculation unit 140 calculates left / right (2)×4 joints×3 directions=24 pieces of joint angle data 173.

[0054] The walking evaluation score calculation unit 150 includes a joint score calculation unit 151 and an evaluation score calculation processing unit 152 .

[0055] The joint score calculation unit 151 calculates a joint score 174, which is an evaluation score of the movement of the joint in one walking cycle, from the joint angle data 173 in one walking cycle (step S11 in FIG. 2). More specifically, the joint score calculation unit 151 calculates a right side joint score 174a in one walking cycle based on the right side joint angle data 173a in one walking cycle and its reference data 178, and calculates a left side joint score 174b in one walking cycle based on the left side joint angle data 173b and its reference data 178, and stores these in the storage unit 170.

[0056] As described above, each of the joint angle data 173a, 173b is composed of 12 pieces of joint angle data corresponding to each joint and each direction, so the joint score calculation unit 151 calculates 12 pieces of joint scores 174a, 174b corresponding to each of the joint angle data 173a, 173b. In this embodiment, each of the joint scores 174 has 101 values ​​at 1% intervals in the time axis direction within one walking cycle.

[0057] The reference data 178 is calculated using the right joint angle data 173a and the left joint angle data 173b of a plurality of subjects measured in advance as samples. The reference data 178 is data that defines the probability distribution of the joint angle at each time within one walking cycle of the sample. In this embodiment, it is assumed that the probability distribution of each joint angle data 173a, 173b at each time t is a normal distribution, and the average value μ(t) and standard deviation σ(t) at each time t are used as the reference data 178. This reference data 178 is stored in the storage unit 170 in advance for each joint and each direction. In this embodiment, 12 pieces of reference data 178 corresponding to each joint and each direction are stored. This reference data 178 is used as a standard for good walking.

[0058] The processing of the joint score calculation unit 151 will be described below. The processing is the same for the left and right, so the processing for the right side will be described as an example with reference to Fig. 12. Fig. 12 is a diagram for explaining the calculation of the joint score for the right hip joint sagittal plane. In Fig. 12, the reference data shows the average value μ.

[0059] The joint score calculation unit 151 calculates a joint score (t) at each time t within one walking cycle based on the difference (t) between the angle data (t) in the right joint angle data 173a and the average value μ(t) included in the reference data 178, and the difference d(t) and the standard deviation σ(t) included in the reference data 178. Here, the joint score calculation unit 151 calculates the score so that the closer the angle data (t) is to the average value μ(t), the higher the score, that is, the closer the difference d(t) is to zero, the higher the score. More specifically, the joint score calculation unit 151 calculates the score so that the smaller |difference d(t) / standard deviation σ(t)| is, the higher the score, and the larger |difference d(t) / standard deviation σ(t)| is, the lower the score.

[0060] For example, if the maximum joint score is 100 points, then if angle data(t) = average value μ(t), then score(t) = 100; if angle data(t) = average value μ(t) ± standard deviation σ(t), then score(t) = 32; and if angle data(t) = average value μ(t) ± 2 × standard deviation σ(t), then score(t) = 5 points.

[0061] The joint score calculation unit 151 performs a similar calculation process to calculate a left joint score 174b based on the left joint angle data 173b and the reference data 178, and stores the calculated joint score 174b in the storage unit 170. Note that the reference data 178 used in this calculation process is common to both the left and right.

[0062] Through the above process, the joint score calculation unit 151 calculates left / right (2) x 4 joints x 3 directions = 24 joint scores 174.

[0063] The evaluation score calculation processing unit 152 calculates various walking evaluation scores 175 useful for evaluating walking based on each joint score 174 a and 174 b , and stores them in the storage unit 170 .

[0064] In this embodiment, the evaluation score calculation processing unit 152 calculates, as the walking evaluation score 175, left / right difference evaluation scores 175a and 175b for evaluating the left / right difference in walking, a posture evaluation score 175c for evaluating the posture while walking, a joint movement evaluation score 175d for evaluating the movement of the joints while walking, and a muscle strength evaluation score 174e for evaluating the muscle strength related to walking, as shown in FIG. 13.

[0065] The left-right difference evaluation score includes a posture left-right difference evaluation score 175a that evaluates the left-right difference in posture during walking, and a joint left-right difference evaluation score 175b that evaluates the left-right difference in joint movement during walking.

[0066] The posture left-right difference evaluation score 175a is a score related to the distortion of the base (lower body) of the human body; the higher the score, the smaller the posture left-right difference, and therefore the smaller the distortion of the base. On the other hand, the smaller the posture left-right difference evaluation score 175a, the larger the posture left-right difference, and therefore the larger the distortion of the base. The evaluation score calculation processing unit 152 first calculates the absolute value of the difference between the right joint score 174a and the left joint score 174b at each time within one walking cycle for each joint and each direction, and calculates the average value of the difference within one walking cycle. The calculation formula for the left-right difference evaluation score (joint, direction) for each (joint, direction) is as follows:

[0067]

number

[0068] Here, T is the number of samples, which is 101 in this embodiment.

[0069] Next, the evaluation score calculation processing unit 152 focuses on the base (lower body) of the human body and calculates the left-right posture difference evaluation score 175a by the following formula.

[0070]

number

[0071] As shown in the above formula, the posture left-right difference evaluation score 175a is a simple arithmetic average of the left-right difference evaluation score related to (pelvis, horizontal plane) and the left-right difference evaluation score related to (pelvis, frontal plane), but it may be calculated by an appropriate weighted average. The posture left-right difference evaluation score 175a is a useful evaluation score for comprehensively evaluating the left-right difference in posture, but the left-right difference evaluation scores (joints, direction) of (pelvis, horizontal plane) and (pelvis, frontal plane) used in this calculation are also useful evaluation scores, so these are also included in the walking evaluation score 175a.

[0072] The joint left-right difference evaluation score 175b is a score that evaluates the left-right difference in joint movement during walking, that is, a score that evaluates the bias in how the body is used. The higher the joint left-right difference evaluation score 175b, the smaller the left-right difference in joint movement, and therefore the smaller the bias in how the body is used. On the other hand, the lower the score, the larger the left-right difference in joint movement, and the larger the bias in how the body is used. The evaluation score calculation processing unit 152 focuses on the movement of each joint in the sagittal plane and calculates the joint left-right difference evaluation score 175b using the following formula.

[0073]

number

[0074] As shown in the above formula, the joint left-right difference evaluation score 175b is a simple arithmetic average of the left-right difference evaluation scores of each joint (excluding the pelvis) in the sagittal plane, but it may be calculated by an appropriate weighted average. The joint left-right difference evaluation score 175b is a useful evaluation score for comprehensively evaluating the left-right difference in joint movements, but the left-right difference evaluation scores (joint, direction) of (hip joint, sagittal plane), (knee joint, sagittal plane), and (ankle joint, sagittal plane) used in this calculation are also useful evaluation scores, so these are also included in the joint left-right difference evaluation score 175b.

[0075] The posture evaluation score 175c is a score that evaluates the posture during walking, and is a score that evaluates the stability of the base (lower body) of a person. The higher the posture evaluation score 175c, the more stable the base is, and the lower the score, the less stable the base is. The evaluation score calculation processing unit 152 first calculates the average value of the right joint score 174a and the left joint score 174b at each time within one walking cycle for each joint and each direction, and calculates the average value within one walking cycle of the average values. The calculation formula for the left and right average evaluation score (joint, direction) for each (joint, direction) is as follows.

[0076]

number

[0077] Next, the evaluation score calculation processing unit 152 focuses on the base (lower body) of the person and calculates the posture evaluation score 175c by the following formula.

[0078]

number

[0079] As shown in the above formula, the posture evaluation score 175c is a simple arithmetic average of the left and right average evaluation scores in each direction of the pelvis, but it may be calculated by an appropriate weighted average. The posture evaluation score 175c is a useful evaluation score for comprehensively evaluating the posture during walking, but the left and right average evaluation scores (joints, directions) of (pelvis, sagittal plane), (pelvis, frontal plane), and (pelvis, horizontal plane) used in this calculation are also useful evaluation scores, so these are also included in the posture evaluation score 175c.

[0080] The joint movement evaluation score 175d is a score that evaluates how the body is used when walking. The higher the joint movement evaluation score 175d, the better the body is used, and the lower the score, the worse the body is used. The evaluation score calculation processing unit 152 focuses on the movement of each joint in the sagittal plane and calculates the joint movement evaluation score 175d using the following formula.

[0081]

number

[0082] As shown in the above formula, the joint movement evaluation score 175d is a simple arithmetic average of the left and right average evaluation scores of each joint (excluding the pelvis) in the sagittal plane, but it may be calculated by an appropriate weighted average. The joint movement evaluation score 175d is a useful evaluation score for comprehensively evaluating the movement of the joints during walking, but the left and right average evaluation scores (joint, direction) of (hip joint, sagittal plane), (knee joint, sagittal plane), and (ankle joint, sagittal plane) used in this calculation are also useful evaluation scores, so these are also included in the posture evaluation score 175c.

[0083] The calculation of the muscle strength evaluation score 175e will be described. The muscle strength evaluation score 175e is an evaluation score for a muscle (group) related to the movement of a joint used in walking. In the present invention, when evaluating a muscle (group), a joint score for a joint related to the muscle (group) is used.

[0084] FIG. 14 is a graph showing the sagittal plane joint angles of the hip joint, knee joint, and ankle joint, and the timing of muscle activity of the muscles associated with each joint. As shown in FIG. 14, among the major muscles of a human, the muscle activities in the sagittal plane direction of the gluteus maximus (lower part), adductor magnus, biceps femoris (long head), semimembranosus, and semitendinosus are active at the timing of extension of the hip joint in the sagittal plane. Here, these muscle groups are called hip joint extensors. Also, among the major muscles of a human, the rectus femoris, iliacus, adductor longus, gracilis, and sartorius are active at the timing of flexion of the hip joint in the sagittal plane. Here, these muscle groups are called hip joint flexors.

[0085] Similarly, as shown in Fig. 15, among the major muscles of a human being, a muscle group that is active at the timing of extension of the knee joint in the sagittal plane is called the knee joint extensor muscles. Also, among the major muscles of a human being, a muscle group that is active at the timing of flexion of the knee joint in the sagittal plane is called the knee joint extensor muscles.

[0086] Similarly, as shown in Fig. 16, among the major muscles of a human, a muscle group that is active at the timing of dorsiflexion of the ankle joint in the sagittal plane is called the ankle dorsiflexors. Also, among the major muscles of a human, a muscle group that is active at the timing of plantar flexion of the ankle joint in the sagittal plane is called the ankle plantar flexors.

[0087] Based on the above knowledge, the evaluation score calculation processing unit 152 in this embodiment calculates individual muscle strength evaluation scores for six muscles, namely, hip extensors, hip flexors, knee extensors, knee flexors, ankle dorsiflexors, and ankle plantar flexors, for each of the right and left sides, and calculates an overall muscle strength evaluation score 175e based on the individual muscle strength evaluation scores.

[0088] The evaluation score calculation processing unit 152 sets a muscle force exertion period 179 in advance on the graphs shown in Figs. 14 to 16, and stores the muscle force exertion period 179 in the storage unit 170. The muscle force exertion period 179 is a period during one walking cycle during which each muscle that affects walking exerts force for the joint and direction corresponding to the muscle. The muscle force exertion period 179 is common to both the left and right. An example of the muscle force exertion period 179 is shown in Fig. 17.

[0089] The evaluation score calculation processing unit 152 calculates the average value of each joint score during the muscle strength exertion period 179, which is the joint score 174 for the joints related to the right or left muscles, and sets this average value as the individual muscle strength evaluation score for each left or right muscle. The individual muscle strength evaluation score is calculated by the following formula.

[0090]

number

[0091] Here, Tm is the number of samples during the strength exertion period.

[0092] The evaluation score calculation processing unit 152 further calculates an overall muscle strength evaluation score 175e by statistically aggregating the individual muscle strength evaluation scores for each of the right and left muscles. Examples of aggregating methods include a simple arithmetic average and a weighted average. In the following formula, the simple arithmetic average is used.

[0093]

number

[0094] In addition, the muscle strength evaluation score 175e is an evaluation score that is useful for comprehensively evaluating muscle strength during walking, but the individual muscle strength evaluation scores used in this calculation are also useful evaluation scores, so these are also included in the muscle strength evaluation score 175e.

[0095] The exercise menu presenting unit 160 selects an exercise menu corresponding to the evaluation score calculated by the evaluation score calculation processing unit 152 (posture left-right difference evaluation score 175a and joint left-right difference evaluation score 175b) when the evaluation score is equal to or less than a predetermined threshold value according to the posture evaluation score 175c, the joint movement evaluation score 175d, and the muscle strength evaluation score 175e, and presents the selected exercise menu to the subject by displaying it on the display device 300. Here, the predetermined threshold value may be set for each evaluation score of the evaluation target, may be set for each group consisting of multiple evaluation scores, or may be common to all evaluation scores. In addition, the exercise menu corresponding to the evaluation score is set in advance to be effective for improving the evaluation score. In addition, the exercise menu can be presented corresponding to the evaluation score for each joint or muscle included in each evaluation score.

[0096] In addition, the exercise menu presentation unit 160 repeats the process of comparing the evaluation scores with a predetermined threshold corresponding to the evaluation scores in the order of left-right difference evaluation score (posture left-right difference evaluation score and joint left-right difference evaluation score), posture evaluation score, and joint movement evaluation score until an evaluation score equal to or less than the predetermined threshold is found, and selects only the exercise menu corresponding to the first evaluation score equal to or less than the predetermined threshold. When all of the left-right difference evaluation score (posture left-right difference evaluation score and joint left-right difference evaluation score), posture evaluation score, and joint movement evaluation score are greater than the predetermined threshold, the exercise menu presentation unit 160 selects an exercise menu corresponding to the muscle strength evaluation score. That is, the presented exercise menu is presented in the order of those effective for improving left-right difference, those effective for improving walking posture, those effective for joint movement during walking, and those effective for increasing muscle strength, which should be implemented from now on, according to the functional improvement status of the subject's walking due to exercise. In addition, this order determines in which order the walking evaluation score should be improved for better walking.

[0097] The exercise menu presentation unit 160 stores the exercise menu 176 shown in the table below in advance in the storage unit 170. As shown in the example of the table below, multiple exercise menus can be set for one evaluation score. Note that there are also evaluation scores that do not have a corresponding exercise menu. An example of the exercise menu 176 is shown in the table below.

[0098] [Table 1]

[0099] When there are multiple candidates for the exercise menu corresponding to the evaluation score, the exercise menu presentation unit 160 can select multiple exercise menus that are less than the number of candidates. When there are multiple candidates for the exercise menu corresponding to the evaluation score, the exercise menu presentation unit 160 selects an exercise menu different from the previous selection from the exercise menu candidates. Here, when presenting multiple exercise menus that are less than the number of candidates, "selecting a different exercise menu" means that at least one of the multiple exercise menus presented was not selected previously. In other words, it is permissible to present the exercise menu presented previously this time. Examples of algorithms for selecting a different exercise menu include round robin selection and random selection.

[0100] An example of the operation of the exercise menu presenting unit 160 is shown in the flowcharts of Figures 18 to 20. The examples of Figures 18 to 20 are based on Table 1 above, and the threshold value is common to each evaluation score.

[0101] In this operation example, the exercise menu presenter 160 evaluates in the following order: left-right difference evaluation score (pelvis, frontal plane), which is one of the posture left-right difference evaluation scores 175a; left-right difference evaluation score (pelvis, horizontal plane); left-right difference evaluation score (hip joint, sagittal plane), which is one of the joint left-right difference evaluation scores b; left-right average evaluation score (pelvis, sagittal plane), left-right average evaluation score (pelvis, frontal plane), left-right average evaluation score (pelvis, horizontal plane), left-right average evaluation score (pelvis, frontal plane), which is one of the joint movement evaluation scores 175d; and left-right average evaluation score (ankle, sagittal plane) (steps S101, S103, S105, S107, S111, S113, S115).

[0102] If the evaluation score is equal to or less than a predetermined threshold, the exercise menu presentation unit 160 selects and presents an exercise menu corresponding to the evaluation score (steps S102, S104, S106, S108, S110, S112, S114, S116). On the other hand, if all of the above-mentioned evaluation scores are greater than the predetermined threshold, an exercise menu corresponding to the muscle strength evaluation score is selected and presented (step S117). In the process of selecting and presenting each exercise menu, an exercise menu different from the one previously selected is selected and presented in a round robin manner.

[0103] According to the exercise support device 100 of this embodiment, the walking evaluation score 175 is calculated based on the acceleration data of the subject while walking. This acceleration data can be easily and inexpensively acquired by the acceleration sensor 200. Therefore, the walking evaluation score of the subject can be analyzed simply and inexpensively. Each calculated walking evaluation score 175 can be used in the exercise menu presentation unit 160 as in the above embodiment, or can be output to the display device 300, output to another device, or various processes based on each walking evaluation score 175, and can be used in any manner.

[0104] In addition, in the exercise support device 100 according to this embodiment, right and left joint scores 174 are calculated based on right and left joint angle data 173 calculated from acceleration data and its reference data 178, and a gait evaluation score 175 is calculated from these joint scores 174, so that various quantitative and useful gait evaluation scores based on the index of the reference data 178 can be obtained.

[0105] In addition, the exercise support device 100 according to this embodiment calculates the left-right difference evaluation scores 175a and 175b, the walking posture evaluation score 175c, the joint movement evaluation score 175d, and the muscle strength evaluation score 175e, selects an exercise menu corresponding to each calculated walking evaluation score 175, and presents the selected exercise menu to the subject. Therefore, it is possible to efficiently and effectively perform exercise for better walking corresponding to the walking evaluation score to be improved.

[0106] In addition, in the exercise support device 100 according to the present embodiment, the comparison process between the evaluation scores and the predetermined thresholds corresponding to the evaluation scores is repeated in the order of the left-right difference evaluation scores 175a, 175b, the posture evaluation score 175b, and the joint movement evaluation score 175c until an evaluation score below the predetermined threshold is found, and the exercise menu corresponding to the first evaluation score below the predetermined threshold is selected, and if an evaluation score below the predetermined threshold is not found, an exercise menu corresponding to the muscle strength evaluation score is selected. That is, according to the functional improvement status of the subject's walking due to exercise, the exercise menu to be performed from now on is presented in the order of those effective for improving the left-right difference, those effective for improving the walking posture, those effective for the joint movement during walking, and those effective for increasing muscle strength. This allows the walking function to be improved efficiently and effectively.

[0107] In addition, in the exercise support device 100 according to the present embodiment, when there are multiple exercise menus corresponding to the evaluation item, an exercise menu different from the previously presented exercise menu is presented. This allows the subject to enjoy exercising without getting bored.

[0108] Although one embodiment of the present invention has been described in detail above, the present invention is not limited to the above embodiment, and various improvements and modifications may be made without departing from the spirit and scope of the present invention.

[0109] For example, in the above embodiment, the walking evaluation score calculation device is implemented as part of the exercise support device, but the walking evaluation score calculation device may be implemented as a device that does not have the function of selecting and displaying an exercise menu.

[0110] In the above embodiment, the posture left-right difference evaluation score 175a, the joint left-right difference evaluation score 175b, the posture evaluation score 175c, the joint movement evaluation score 175d, and the muscle strength evaluation score 174e are all calculated as the walking evaluation score 175, but any one of these walking evaluation scores may be calculated. In the above embodiment, each walking evaluation score 175 includes not only a comprehensive evaluation score but also individual evaluation scores according to the joints, directions, muscles, etc. used in the calculation of the evaluation score, but only any one of the individual evaluation scores may be calculated.

[0111] In addition, in the above embodiment, the exercise menu presenting unit 160 associates the individual evaluation scores included in the overall evaluation score with the exercise menu, but it may also be possible to associate the overall evaluation score with the exercise menu.

[0112] In addition, in the above embodiment, one piece of reference data 178 is used, but multiple pieces of reference data may be prepared in advance according to gender and age group, and the reference data 178 to be used may be switched to the reference data corresponding to the subject according to the gender and age group of the subject.

[0113] In the above embodiment, the analysis parameters 177, the reference data 178, and the muscular strength exertion period 179 are stored in advance in the storage unit 170. However, a predetermined management server or storage may be accessed via a network for each measurement process or periodically to obtain the latest data from the management server or the like.

[0114] In the above embodiment, the exercise support device 100 is implemented as a single device with the acceleration sensor 200, the acceleration data acquisition unit 110, the data range setting unit 120, the walking cycle determination unit 130, the joint angle data calculation unit 140, the walking evaluation score calculation unit 150, the exercise menu presentation unit 160, the storage unit 170, and the display unit 300, but they can be implemented in any combination and distributed across multiple devices. For example, the device for acquiring acceleration data, the device for calculating the walking evaluation score, and the device for selecting and presenting the display menu can each be implemented as separate devices. [Explanation of symbols]

[0115] 100...Exercise support device 110...Acceleration data acquisition unit 120...Data range setting section 130...Walking cycle determination unit 140…Walking posture analysis section 150…Walking evaluation score calculation section 151...Joint score calculation unit 152...Evaluation score calculation processing unit 160…Exercise menu display section 170...Storage section

Claims

1. an acceleration data acquisition unit that acquires acceleration data of the subject while walking measured by an acceleration sensor fixed to the subject; a joint angle data calculation unit that calculates right joint angle data, which is joint angle data of the right half of the body, and left joint angle data, which is joint angle data of the left half of the body, in one walking cycle based on the acquired acceleration data; a joint score calculation unit that calculates a right joint score in one walking cycle based on the calculated right joint angle data in one walking cycle and reference data thereof, and calculates a left joint score in one walking cycle based on the calculated left joint angle data in one walking cycle and reference data thereof; and a gait evaluation score calculation unit that calculates a gait evaluation score based on the calculated right joint score and left joint score. A walking evaluation score calculation device characterized by the above.

2. The gait evaluation score calculation unit includes a left-right difference evaluation score calculation unit that calculates a left-right difference evaluation score that is an average value of differences between the right joint score and the left joint score within one gait cycle.

2. The walking evaluation score calculation device according to claim 1,

3. the joint angle data calculation unit calculates at least pelvis joint angle data in a plurality of directions; The gait evaluation score calculation unit calculates a posture left-right difference evaluation score, which is an evaluation score regarding the left-right difference in posture, based on the left-right difference evaluation scores of the pelvis in the multiple directions calculated by the left-right difference evaluation score calculation unit.

3. The walking evaluation score calculation device according to claim 2,

4. the joint angle data calculation unit calculates joint angle data in a sagittal plane of at least a hip joint, a knee joint, and an ankle joint; The gait evaluation score calculation unit calculates a joint left-right difference evaluation score, which is an evaluation score regarding the left-right difference in joint movement, based on the left-right difference evaluation scores in the sagittal plane for the multiple joints calculated by the left-right difference evaluation score calculation unit.

3. The walking evaluation score calculation device according to claim 2.

5. The walking evaluation score calculation unit includes a left-right average evaluation score calculation unit that calculates a left-right average evaluation score that is an average value of the right joint score and the left joint score within one walking cycle.

2. The walking evaluation score calculation device according to claim 1,

6. the joint angle data calculation unit calculates at least pelvis joint angle data in a plurality of directions; The walking evaluation score calculation unit calculates a posture evaluation score, which is an evaluation score of posture, based on the left and right average evaluation scores of the pelvis in the multiple directions calculated by the left and right average evaluation score calculation unit.

6. The walking evaluation score calculation device according to claim 5,

7. the joint angle data calculation unit calculates joint angle data in a sagittal plane of at least a hip joint, a knee joint, and an ankle joint; The gait evaluation score calculation unit calculates a joint movement evaluation score, which is an evaluation score for joint movement, based on the left and right average evaluation scores in the sagittal plane for the plurality of joints calculated by the left and right average evaluation score calculation unit.

6. The walking evaluation score calculation device according to claim 5,

8. The walking evaluation score calculation unit includes a muscle force exertion period storage unit that stores a muscle force exertion period during one walking cycle during which the muscle exerts force for a muscle that affects walking, and a muscle strength evaluation score calculation unit that calculates a muscle strength evaluation score that is an average value of the right joint score or the left joint score for a joint associated with the muscle during the muscle force exertion period.

2. The walking evaluation score calculation device according to claim 1,

9. A method for calculating an evaluation score related to walking using a computer, comprising: acquiring acceleration data of the subject while walking, the acceleration data being measured by an acceleration sensor fixed to the subject; calculating right side joint angle data, which is joint angle data of the right half of the body, and left side joint angle data, which is joint angle data of the left half of the body, during one walking cycle based on the acquired acceleration data; calculating a right joint score in one walking cycle based on the calculated right joint angle data in one walking cycle and its reference data, and calculating a left joint score in one walking cycle based on the calculated left joint angle data in one walking cycle and its reference data; and calculating a walking evaluation score based on the calculated right joint score and left joint score. A method for calculating a walking evaluation score.

10. A computer is caused to function as the walking evaluation score calculation device according to claim 1. A walking evaluation score calculation program comprising:

Images