JP6928355B2 - Walking / foot evaluation device, walking / foot evaluation program, operation method of walking / foot evaluation device, and walking / foot evaluation system - Google Patents

Description

The present invention relates to a walking / foot evaluation device , a walking / foot evaluation program , an operation method of the walking / foot evaluation device, and a walking / foot evaluation system.

In the related field, inventions relating to the development of a device for detecting human sole pressure have been made (Patent Documents 1 and 2). In addition, the technique of measuring the shape of the foot in a stationary standing state and supporting shoe selection from the data of the foot arch height (Patent Document 3) and the sample insole with different fitting points and raised heights force walking. A technique (Patent Document 4) has also been proposed in which the fitting point and the height of excitement are determined by comparing the unbalanced walking motion data with the acceleration measured by the user.

So far, the present inventors have developed a measuring device for evaluating plantar pressure, and reported on its usefulness and analysis results of the obtained data (Non-Patent Documents 1 to 3). That is, development of a measuring device for evaluating sole pressure and verification of its effectiveness (Non-Patent Document 1), and investigation on the relationship between the foot arch structure and sole pressure using the developed device (Non-Patent Document 2). , We proceeded with the investigation of the relationship between the characteristics of the sole pressure value and the fall history of the elderly (Non-Patent Document 3), proposed a mechanism that can evaluate the condition of the foot in daily life, and the characteristic amount of the obtained data. Has been evaluated.

Japanese Patent Application Laid-Open No. 2011-505015 Japanese Unexamined Patent Publication No. 2009-254811 W02005 / 006905 JP-A-2007-144147

M. Saito. K. Nakajima, C. Takano, Y. Ohta, C. Sugimoto, R. Ezoe, K. Sasaki, H. Hosaka, T. Ifukube, S. Ino, K. Yamashita. An in-shoe device to measure planter pressure during daily human activity, Medical Engineering & physics, Vol. 33, pp.638-645, 2011. E. Anzai, K. Nakajima, Y. Iwakami, M. Sato, S. Ino, T. Ifukube, K. Yamashita and Y. Ohta. Effects of Foot Arch Structure on Postural Stability, Clinical Research on Foot & Ankle, 2: 2, 2014 (Open access). K. Nakajima, E. Anzai, Y. Iwakami, S. Ino, K. Yamashita, Y. Ohta. Measuring gait pattern in elderly individuals by using a plantar pressure measurement device, Technology and Health Care, 22 (6), pp. 805-15, 2014.

In the conventional technology described above, it was possible to acquire data from individual subjects, but it is not considered to collect data of multiple users in a unified manner, and appropriate analysis and evaluation are performed. It wasn't enough on. In addition, the conventional technology uses measurement data in a stationary state and measurement data in a walking state in a trially set environment, and in both cases, data is acquired from walking movements in daily life for analysis and evaluation. Has not been realized.

The present invention has been proposed in view of the above-mentioned conventional problems, and an object of the present invention is to grasp the daily walking state of a user as data, and to collect data of a plurality of users in a unified manner. The purpose is to ensure that appropriate analysis and evaluation are carried out.

In order to solve the above problems, the walking / foot evaluation device according to the present invention has at least a predetermined time during walking and standing still from one or more sensors provided on the insoles of shoes used by a plurality of users. a data acquisition unit for acquiring measurement data of sole pressure, by analyzing the measurement data, for each user, at least plantar pressure parameters during the walking, the foot pressure center foot pressure center parameter indicating the bending angle of the trajectory, It also has an analysis unit that generates and accumulates post-walking data including time parameters, and an evaluation unit that evaluates the inner and outer legs based on the post-walking data.

In the present invention, data of a plurality of users are uniformly collected from information obtained from the daily walking movements of the users, and appropriate analysis / evaluation is performed.

It is a figure which shows the structural example of the system which concerns on one Embodiment of this invention. It is a figure (the 1) which shows the structural example of various data. It is a figure (the 2) which shows the structural example of various data. It is a figure (the 3) which shows the structural example of various data. It is a figure (the 4) which shows the structural example of various data. It is a figure (No. 5) which shows the structural example of various data. It is a figure (No. 6) which shows the structural example of various data. It is a figure (7) which shows the structural example of various data. It is a figure which shows the example of a sensor position. It is a figure which shows the hardware configuration example which concerns on the information processing of a measuring device, an information terminal, a server device, and a management terminal. It is a flowchart which shows the processing example of the stance phase data analysis part. It is a figure (the 1) which shows the example of the waveform data. It is a figure (the 2) which shows the example of the waveform data. It is a figure which shows the example of the extraction of the sole pressure parameter at the time of walking. It is a figure which shows the example of the extraction of the COP parameter at the time of walking. It is a figure which shows the example of the extraction of the time parameter at the time of walking. It is a flowchart which shows the processing example of the static standing data analysis part. It is a figure which shows the example of the extraction of the sole pressure parameter and COP parameter in the static standing position. It is a flowchart which shows the processing example of the forefoot part local load evaluation part. It is a flowchart which shows the processing example of the supination foot / supination foot evaluation part. It is a flowchart which shows the processing example of the multi-step balance evaluation part. It is a figure (the 1) which shows the example of the variation of COP. It is a figure (the 2) which shows the example of the variation of COP. It is a flowchart which shows the processing example of the stance time evaluation part. It is a flowchart which shows the processing example of the both leg support ratio evaluation part. It is a flowchart which shows the processing example of the laterality evaluation part. It is a flowchart which shows the processing example of the foot arch evaluation part. It is a flowchart which shows the processing example of the hallux grounding evaluation part. It is a flowchart which shows the processing example of the comprehensive evaluation part. It is a figure which shows the example of the graph of the comprehensive evaluation result. It is a flowchart which shows the recommended processing example of a shoe. It is a flowchart which shows the processing example of the target data determination with the same walking function.

Hereinafter, preferred embodiments of the present invention will be described.

<Structure>
FIG. 1 is a diagram showing a configuration example of a system according to an embodiment of the present invention. In FIG. 1, a measuring device (shoe device) 2 is provided on the shoes 1 (left and right) used by the user, and the signal data detected by the sensor unit 21 is transmitted via the communication unit 22 via Bluetooth (registered trademark). , A wireless communication means such as a wireless LAN (Local Area Network) is used to transmit data to an information terminal 3 such as a smartphone, a tablet, or a PC (Personal Computer). The data transmission interval from the measuring device 2 to the information terminal 3 is, for example, 10 ms (milliseconds, 100 Hz). The sensor unit 21 is provided with, for example, a plurality of pressure sensors 212 (seven on one leg in the illustrated example) on an insole (insole) type base material 211. In addition to the pressure sensor, a shearing force (friction force) sensor, an acceleration sensor, a temperature sensor, a humidity sensor, or the like may be provided. Further, the insole is provided with a mechanism for changing the color (a mechanism for giving a visual stimulus) and a mechanism for deforming the material or changing the hardness (a mechanism for giving a tactile stimulus) under control from the information terminal 3 side. By doing so, the state of walking and the foot may be fed back to the user. In addition, the communication unit 22 has a function of transmitting position data by GPS (Global Positioning System) or the like. The position data may be acquired from the information terminal 3 instead of the measuring device 2.

The information terminal 3 transmits the data received from the measurement device 2 and temporarily stored to the server device 5 at predetermined time intervals (for example, 10 s) via a network 4 such as a mobile wireless network or the Internet. Has the function of Further, the information terminal 3 acquires information on the user's walking and foot condition from the server device 5 and displays the screen, and provides the user with feedback on the walking and foot condition, support for shoe selection, and the like. It also has a function. Although the description has been given to the case where data is transmitted from the measurement device 2 to the server device 5 via the information terminal 3, the data is directly transmitted from the measurement device 2 to the server device 5 depending on the environment. It may be a thing. In this case, the information terminal 3 is used for operating the measuring device 2, providing feedback to the user, and the like.

The server device 5 has a basic data input unit 501, a measurement data receiving unit 502, a data analysis unit 503, a life log writing unit 506, an evaluation unit 507, a comprehensive evaluation unit 515, and a comprehensive analysis unit 516 as parts that realize a processing function. And have. In addition, a database 521 for storing data used in processing is provided. The database 521 may be held and managed separately from the server device 5.

The database 521 holds user data 522, shoe data 523, life log data 524, position data 525, measurement data 526, walking processing data 527, and stationary standing data 528. The user data 522 and the shoe data 523 are basic data such as a user and shoes. The life log data 524 is data indicating user actions (including schedules). The position data 525 is data acquired from the measurement device 2 or the information terminal 3, and the measurement data 526 is data acquired from the measurement device 2. The data after walking processing 527 and the data after standing still standing processing 528 are data obtained by analysis from the measurement data 526.

As illustrated in FIG. 2, the user data 522 includes "user ID", "name", "shoe ID", "gender", "date of birth", "height", "weight", "shoe size", "foot length", and "foot width". It has items such as "foot height", "foot circumference", "registration date", and "renewal date". As illustrated in FIG. 3, the shoe data 523 includes "shoe ID", "user ID", "purchase date", "use start date", "shoe store ID", "shoe manufacturer model number", "shoe type", "theme", and "shoe rubbing". It has items such as "presence / absence", "shoe rubbing site", "presence / absence of callus", "presence / absence of pain", "presence / absence of pain", "presence / absence of compression", "presence / absence of compression", "registration date", and "update date". "Presence / absence of shoe rubbing", "presence / absence of shoe rubbing", "presence / absence of callus", "presence / absence of pain", "presence / absence of pain", "presence / absence of compression", "presence / absence of compression", etc. are input by the user, for example.

As illustrated in FIG. 4, the life log data 524 includes "log ID", "date and time", "user ID", "plan for one day", "destination", "moving distance", "number of steps", and "average walking speed". It has items such as "most location information (GPS)", "registration date", and "update date".

As illustrated in FIG. 5, the position data 525 has items such as "date and time", "user ID", and "position information (GPS)".

As illustrated in FIG. 6, the measurement data 526 includes "date and time", "user ID", "left foot 1st sensor: hindfoot (heel) pressure value", and "left foot 2nd sensor: midfoot 1). "Pressure value" "Left foot 3rd sensor: Forefoot 1) Pressure value" "Left foot 4th sensor: Forefoot 2 pressure value" "Left foot 5th sensor: Forefoot 3 pressure value" "Left foot 6th sensor: Midfoot 2 "Pressure value" "Left foot 7th sensor: Forefoot 4 pressure value" "Right foot 1st sensor: Hindfoot (heel) pressure value" "Right foot 2nd sensor: Midfoot 1 pressure value" "Right foot 3rd sensor: Forefoot Part 1 pressure value "" Right foot 4th sensor: Forefoot 2 pressure value "" Right foot 5th sensor: Forefoot 3 pressure value "" Right foot 6th sensor: Midfoot 2 pressure value "" Right foot 7th sensor: Forefoot part 7 4 Pressure value "" Both foot pressure center COP_X coordinate value "" Both foot pressure center COP_Y coordinate value "" Left foot pressure center COP_X coordinate value "" Left foot pressure center COP_Y coordinate value "" Right foot pressure center COP_X coordinate value " It has items such as "right foot pressure center COP_Y coordinate value". The sensor numbers are as shown in FIG. The waveform data is continuous data of pressure values for a time (for example, 10 s) accumulated in the information terminal 3 in a sampling period (for example, 10 ms). The COP is the Center of Pressure. "Left foot No. 1 sensor: hind foot (heel) pressure value" and the like show waveform data of the pressure value. "Both foot pressure center COP_X coordinate value" and the like show the locus of the coordinate value.

As illustrated in FIG. 7, the post-walking data 527 includes “date time”, “user ID”, “step count”, “left foot average stance time”, “right foot average stance time”, “both leg support ratio”, and “left foot single leg”. "Support ratio" "Right foot single leg support ratio" "Left foot 1st sensor: Hindfoot (heel) maximum pressure value average" "Left foot 2nd sensor: Midfoot 1 maximum pressure value average" "Left foot 3rd sensor: Forefoot part 1 Maximum pressure value average ”“ Left foot 4th sensor: Forefoot 2 maximum pressure value average ”“ Left foot 5th sensor: Forefoot 3 maximum pressure value average ”“ Left foot 6th sensor: Midfoot 2 maximum pressure value average ”“ Left foot 7th sensor: Forefoot 4 maximum pressure value average "Right foot 1st sensor: Hindfoot (heel) maximum pressure value average" "Right foot 2nd sensor: Midfoot 1 maximum pressure value average" "Right foot 3rd sensor" : Forefoot 1 maximum pressure value average "" Right foot 4th sensor: Forefoot 2 maximum pressure value average "" Right foot 5th sensor: Forefoot 3 maximum pressure value average "" Right foot 6th sensor: Midfoot 2 maximum pressure value "Average" "Right foot 7th sensor: Average of forefoot 4 maximum pressure value" "COP start position X coordinate" "COP start position Y coordinate" "COP end position X coordinate" "COP end position Y coordinate" "Left foot COP flexion angle" It has items such as "right foot COP flexion angle", "left foot walking mid-term COP_X coordinate range", and "right foot walking mid-term COP_X coordinate range". "Left foot No. 1 sensor: average maximum pressure value of hind foot (heel)" and the like are sole pressure parameters. "Left foot COP flexion angle", "left foot walking mid-term COP_X coordinate range", etc. are COP parameters. "Left foot average stance time", "Right foot average stance time", "Both leg support ratio", "Left foot single leg support ratio", and "Right foot single leg support ratio" are time parameters.

As illustrated in FIG. 8, the data 528 after the stationary standing process includes "date and time", "user ID", "left foot No. 1 sensor: hind foot (heel) load ratio", and "left foot No. 2 sensor: middle". "Foot 1 load ratio" "Left foot 3rd sensor: Forefoot 1 load ratio" "Left foot 4th sensor: Forefoot 2 load ratio" "Left foot 5th sensor: Forefoot 3 load ratio" "Left foot 6th sensor: Middle foot "Part 2 load ratio" "Left foot 7th sensor: Forefoot 4 load ratio" "Right foot 1st sensor: Hindfoot (heel) load ratio" "Right foot 2nd sensor: Midfoot 1 load ratio" "Right foot 3rd sensor" : Forefoot 1 load ratio "" Right foot 4th sensor: Forefoot 2 load ratio "" Right foot 5th sensor: Forefoot 3 load ratio "" Right foot 6th sensor: Midfoot 2 load ratio "" Right foot 7th sensor: It has items such as "forefoot 4 load ratio", "COP total trajectory length", and "COP rectangular area". "Left foot No. 1 sensor: hind foot (heel) load ratio" and the like are plantar pressure parameters. "COP total locus length" and "COP rectangular area" are COP parameters.

Returning to FIG. 1, the basic data input unit 501 has a function of accepting settings of basic data such as a user and shoes and registering them in the user data 522 and the shoe data 523 of the database 521, respectively.

The measurement data receiving unit 502 has a function of receiving data transmitted from the measuring device 2 via the information terminal 3 and registering the data in the position data 525 and the measurement data 526 of the database 521.

The data analysis unit 503 includes a stance phase data analysis unit 504 and a stationary standing data analysis unit 505, and has a function of generating data that is the basis of evaluation by the subsequent evaluation unit 507. That is, the stance phase data analysis unit 504 analyzes the measurement data 526 and generates the data 527 after the walking process, and the static standing data analysis unit 505 analyzes the measurement data 526 and generates the data 528 after the static standing process. ..

The life log writing unit 506 has a function of recording in the position data 525 and the life log data 524 of the database 521.

The evaluation unit 507 includes a forefoot local load evaluation unit 508, a pronational foot / pronational foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a double leg support ratio evaluation unit 512, and a laterality evaluation unit 513. It is equipped with a foot arch evaluation unit 514 and a hallux ground contact evaluation unit 517, and has a function of performing various evaluations and generating evaluation results. Although it is assumed that the evaluation unit 507 performs the evaluation each time, the evaluation result may be saved in association with the user.

The comprehensive evaluation unit 515 calculates the degree of beauty, body balance, walking rhythm, and activity from various evaluation results by the evaluation unit 507 and life log data 524, and comprehensively evaluates the user's walking / foot. It has a function to visualize and feed back to the user.

The comprehensive analysis unit 516 has a function of performing comprehensive analysis based on the data accumulated in the database 521 in consideration of temporal state changes, and realizing early detection / prediction warning of foot abnormality. For example, even if the possibility of a failure or the like has not been evaluated for the target user at this time, the foot / stationary standing state and walking function are similar from the past data of other users, and other When the possibility of occurrence of a failure or the like is subsequently evaluated by the user, the occurrence of a failure or the like is estimated for the target user in the future, and the fact is fed back. In addition, the comprehensive analysis unit 516 also has a function of giving advice on selecting shoes and proposing a walking method that suits the individual user.

Further, the management terminal 6 operated by the administrator can be connected to the server device 5 via the network 4, and the administrator can check and maintain the data managed by the server device 5.

FIG. 10 is a diagram showing an example of hardware configuration related to information processing of the measurement device 2, the information terminal 3, the server device 5, and the management terminal 6, and shows a general computer configuration. In FIG. 10, the measuring device 2 and the like are CPU (Central Processing Unit) 201, ROM (Read Only Memory) 202, RAM (Random Access Memory) 203, SSD (Solid State Drive) connected to each other via bus 207. / HDD (Hard Disk Drive) 204 is provided. Further, the measurement device 2 and the like include a connection I / F (Interface) 205 and a communication I / F 206.

The CPU 201 comprehensively controls the operation of the measuring device 2 and the like by executing a program stored in the ROM 202 or the SSD / HDD 204 or the like with the RAM 203 as a work area. The functions of the measuring device 2 and the like described with reference to FIG. 1 are realized by executing a predetermined program in the CPU 201. The program may be acquired via a recording medium, may be acquired via a network, or may be embedded in a ROM.

<Processing by stance phase data analysis unit 504>
The stance phase data analysis unit 504 analyzes the measurement data 526 acquired during stance walking and generates post-walking data 527. That is, the stance phase data analysis unit 504 uses waveform data representing the human walking motion to perform walking events such as the stance phase and the swing phase (when a human walking motion is seen, the foot touches the ground. It is automatically detected (divided into a stance phase in which it is in a standing position and a swinging phase in which it is off the ground), and numerical values related to human walking movements are acquired.

FIG. 11 is a flowchart showing a processing example of the stance phase data analysis unit 504. In FIG. 11, the stance phase data analysis unit 504 acquires the waveform data of the pressure value of each sensor of the measurement data 526 (step S111), detects the contact point from the rising edge of the waveform, and cuts out the waveform data for each walk. (Step S112). That is, the state from the point where the pressure values of all the sensors are minimized to the point where the pressure values of all the sensors are minimized is defined as one stance phase, and the stance phase data is the data of the normal walking state. This will be scrutinized under the following conditions, and the data applicable to this will be used for parameter extraction as one stance phase.
(A) A sensor showing the highest pressure value (top maximum value) in the entire waveform data for a certain period of walking is specified, and the plurality of maximum values of the waveform data from the sensor are all 80% based on the top maximum value. Show the above values (b) 1 The length of the stance phase is less than 1200 ms.

FIG. 12 shows an example of waveform data of seven sensors, and waveforms of a plurality of steps are recorded. From these, waveform data for each walk as shown in FIG. 13 is cut out.

Returning to FIG. 11, the stance phase data analysis unit 504 detects the maximum pressure value of the waveform for each step of each sensor, and divides the total maximum pressure value of multiple steps for each sensor by the number of steps to average ( “Left foot No. 1 sensor: average of maximum pressure value of hind foot (heel)”, etc.) is taken and acquired as a sole pressure parameter (step S113). FIG. 14 shows a state in which the maximum value (shown by a black circle) is acquired from the waveform of each sensor.

Returning to FIG. 11, the stance phase data analysis unit 504 then uses the measurement data 526 to perform "left foot pressure center COP_X coordinate value", "left foot pressure center COP_Y coordinate value", "right foot pressure center COP_X coordinate value", and "right foot pressure center COP_X coordinate value". From the "center COP_Y coordinate value", as COP parameters, "COP start position X coordinate", "COP start position Y coordinate", "COP end position X coordinate", "COP end position Y coordinate", "left foot COP flexion angle", "right foot COP flexion angle" "Left foot walking mid-term COP_X coordinate range" "Right foot walking mid-term COP_X coordinate range" is acquired (step S114). FIG. 15 shows a COP locus that passes from point A, which is the COP start position, through point H (the intersection of the straight line connecting the 3rd sensor and the 5th sensor and the COP locus), and passes through point B, which is the COP end position. The angle formed by the straight line AH and the straight line HB is the COP bending angle. In addition, the lateral direction in a plurality of steps of the COP coordinate closest to the midpoint (between the ground contact and the bed leaving) of the COP start position Y coordinate and the COP end position Y coordinate in the Y coordinate direction which is the walking direction (longitudinal direction of the sole). The difference between the maximum value and the minimum value of is the mid-walking COP_X coordinate range.

Returning to FIG. 11, the stance phase data analysis unit 504 then sets the time parameters as "left foot average stance time", "right foot average stance time", "both leg support ratio", "left foot single leg support ratio", and " "Right foot single leg support ratio" is acquired (step S115). FIG. 16 shows the ground contact state of the left and right feet. The period from the contact of one foot to getting out of bed is the stance time, the period in which both feet are in contact with the ground is the period in which both legs are in contact, and the period in which only one foot is in contact with the ground is the single leg support. It will be a period. The two-leg support ratio is the ratio of the time of the two-leg support period to the stance time. The single leg support ratio is the ratio of the time during the single leg support period to the stance time.

Returning to FIG. 11, the stance phase data analysis unit 504 then walks the acquired sole pressure parameter, COP parameter, and time parameter, and the number of steps, which is the number of steps in the stance phase within a certain walking time used at the time of analysis. It is recorded in the processed data 527 (step S116).

<Processing by static standing data analysis unit 505>
The static standing data analysis unit 505 analyzes the measurement data 526 acquired when the standing posture is maintained for a certain period of time, and generates the data 528 after the static standing process.

FIG. 17 is a flowchart showing a processing example of the static standing data analysis unit 505. In FIG. 17, the stationary standing data analysis unit 505 acquires waveform data of the pressure value of each sensor of the measurement data 526 (step S121), and COP locus data (“both foot pressure center COP_X coordinate value” “both foot pressure”. Center COP_Y coordinate value ”) is acquired (step S122). Since the COP locus here is in a state of standing on both feet, it is located near the center of both feet. FIG. 18 shows an example of the COP locus, which fluctuates slightly toward the right foot.

Returning to FIG. 17, the static standing data analysis unit 505 then acquires the total COP trajectory length from the COP trajectory as one of the COP parameters (step S123). That is, the minute movement amount is obtained from the displacement of the x-coordinate and the y-coordinate of the COP for each sampling period of the measurement data 526, and the total locus length of the COP is obtained by integrating them.

Next, the static standing data analysis unit 505 acquires the COP rectangular area as one of the COP parameters (step S124). The COP rectangular area is obtained as the area of the circumscribed rectangle of the COP locus.

Next, the static standing data analysis unit 505 acquires the load ratio of each sensor as one of the plantar pressure parameters (step S125). The load ratio of each sensor is obtained by dividing the average pressure value of each sensor by the sum of the average pressure values of all the sensors (14 sensors on both feet). FIG. 18 shows an example of the value of the load ratio for each sensor (shown in circle).

Returning to FIG. 17, the static standing data analysis unit 505 records the acquired plantar pressure parameter and COP parameter in the static standing data 528 (step S126).

<Processing by forefoot local load evaluation unit 508>
The forefoot local load evaluation unit 508 evaluates the presence or absence of a local load on the forefoot and, by extension, the possibility of abnormal occurrence of calluses, corns, etc., based on the post-walking data 527.

FIG. 19 is a flowchart showing a processing example of the forefoot local load evaluation unit 508, which is an example of evaluating the presence or absence of a local load on the forefoot and determining the possibility of callus generation. Here, the presence or absence of a local load is evaluated by comparing the magnitude of the value for each sensor based on the average of the maximum pressure values from the three sensors on the forefoot.

In FIG. 19, the forefoot portion local load evaluation unit 508 refers to the average maximum pressure value of the forefoot portion (sensors 3, 5, and 7) from the data 527 after the walking process (step S211).

Next, the forefoot local load evaluation unit 508 sets the sensor showing the maximum value among the three sensors and its value as S, and the other two sensors and their values as a and b (step S212).

Next, the forefoot local load evaluation unit 508 determines the load ratio of S and 2a and S and 2b (step S213).

When the forefoot local load evaluation unit 508 does not satisfy the condition that S is larger than 2a and S is larger than 2b (NO in step S213), it is assumed that the load is normal (step S214).

Further, when the condition that S is larger than 2a and S is larger than 2b is satisfied (YES in step S213), the forefoot local load evaluation unit 508 has a local load on the sensor S (step S215). It is evaluated that there is a possibility of callus generation in the area of the sensor S (step S216).

<Processing by the supination / supination evaluation unit 509>
The supination / supination foot evaluation unit 509 evaluates whether there is any abnormality in the supination / supination foot based on the post-walking data 527.

FIG. 20 is a flowchart showing a processing example of the supination foot / supination foot evaluation unit 509. In FIG. 20, the supination / supination foot evaluation unit 509 refers to the COP flexion angle from the post-walking data 527 (step S221).

Next, the supination / supination foot evaluation unit 509 determines whether or not the COP flexion angle is smaller than 155 (step S222), and if it determines that the COP flexion angle is smaller than 155 (YES in step S222), the supination It is evaluated that there is a possibility of supination (step S223).

Further, when the supination / supination foot evaluation unit 509 determines that the COP flexion angle is not smaller than 155 (NO in step S222), it determines whether or not the COP flexion angle is larger than 175 (step S224). When it is determined that the COP flexion angle is larger than 175 (YES in step S224), it is evaluated that there is a possibility of supination (step S225). When the COP flexion angle is not larger than 175 (NO in step S224), the supination / supination foot evaluation unit 509 evaluates as normal (step S226).

<Experimental results>
As a result of evaluating the abnormalities of the supination and supination of 724 people by the supination and supination evaluation unit 509, it was possible to evaluate with an accuracy of 88% to 90%.

<Processing by the multi-step balance evaluation unit 510>
The multi-step balance evaluation unit 510 evaluates the multi-step balance based on the post-walking data 527.

FIG. 21 is a flowchart showing a processing example of the multi-step balance evaluation unit 510. In FIG. 21, the multi-step balance evaluation unit 510 refers to the mid-walking COP_X coordinate range from the post-walking data 527 (step S231), and determines whether or not the mid-walking COP_X coordinate range is larger than 1 (step S232).

Then, when the multi-step balance evaluation unit 510 determines that the COP_X coordinate range in the middle stage of walking is larger than 1 (YES in step S232), it evaluates that the variation in the multi-step balance during walking stance is large (step S233). Further, when the multi-step balance evaluation unit 510 determines that the COP_X coordinate range in the middle stage of walking is not larger than 1 (NO in step S232), it evaluates that the variation in the multi-step balance during walking stance is small (step S234).

22 and 23 are diagrams showing an example of COP variation, FIG. 22 shows an example in which the variation in COP of a plurality of steps is small, and FIG. 23 shows an example in which the variation in COP of a plurality of steps is large.

<Processing by standing time evaluation unit 511>
The stance time evaluation unit 511 evaluates the stance time based on the post-walking data 527.

FIG. 24 is a flowchart showing a processing example of the stance time evaluation unit 511. In FIG. 24, the stance time evaluation unit 511 refers to the stance time as a time parameter from the post-walking data 527 (step S241), and is normal or compared with the normal value of the stance time (for example, 600 to 900 ms). Evaluation such as shorter or longer is performed (step S242).

<Processing by both leg support ratio evaluation unit 512>
The two-leg support ratio evaluation unit 512 evaluates the two-leg support ratio based on the post-walking data 527.

FIG. 25 is a flowchart showing a processing example of the two-leg support ratio evaluation unit 512. In FIG. 25, the two-leg support ratio evaluation unit 512 refers to the two-leg support ratio and the single-leg support ratio as time parameters from the post-walking data 527 (step S251), and obtains normal values for each (for example, the two-leg support ratio). Compared with 20 to 40% and 60 to 80% for the single leg support ratio), evaluation is performed such as normal or less or more (step S252).

<Processing by left-right difference evaluation unit 513>
The left-right difference evaluation unit 513 evaluates the left-right difference in the static standing state based on the data 528 after the static standing process.

FIG. 26 is a flowchart showing a processing example of the laterality evaluation unit 513. In FIG. 26, the left-right difference evaluation unit 513 refers to the load ratios of the left feet 1 to 7 from the data 528 after the static standing process (step S311), and calculates the total left foot load ratio (Sum_Left) (step S312). ..

Further, the left-right difference evaluation unit 513 refers to the load ratios of the right feet 1 to 7 from the data 528 after the static standing process (step S313), and calculates the total right foot load ratio (Sum_Right) (step S314).

Next, the left-right difference evaluation unit 513 determines a value obtained by subtracting the total right foot load ratio (Sum_Right) from the total left foot load ratio (Sum_Left) (step S315), and evaluates that the left foot load is 20 or more (step S315). Step S316), if it is -20 or less, it is evaluated as a right foot load (step S317), and if it is larger than -20 and less than 20, it is evaluated as normal (step S318).

<Processing by foot arch evaluation unit 514>
The foot arch evaluation unit 514 evaluates the formation state of the foot arch in the static standing state based on the data 528 after the static standing process, and evaluates whether or not flat feet may occur.

FIG. 27 is a flowchart showing a processing example of the foot arch evaluation unit 514. In FIG. 27, the foot arch evaluation unit 514 refers to the left foot 2nd sensor: midfoot 1 load ratio and the left foot 6th sensor: midfoot 2 load ratio from the static standing data 528 (step S321). ).

Next, the foot arch evaluation unit 514 compares the load ratios of the left foot second sensor and the sixth sensor (step S322).

When the load ratio of the No. 2 sensor is larger than the load ratio of the No. 6 sensor, the foot arch evaluation unit 514 evaluates that the left foot is normal (step S323), and the load ratio of the No. 2 sensor is equal to or less than the load ratio of the No. 6 sensor. If this is the case, it is evaluated that the foot arch formation of the left foot is insufficient (step S324).

After that, the foot arch evaluation unit 514 refers to the right foot 2nd sensor: midfoot 1 load ratio and the right foot 6th sensor: midfoot 2 load ratio from the static standing data 528 (steps S325 and S326). ).

Next, the foot arch evaluation unit 514 compares the load ratios of the right foot No. 2 sensor and the No. 6 sensor (steps S327 and S328).

In the comparison (step S327) after the foot arch evaluation unit 514 evaluates the left foot as normal (step S323), when the load ratio of the second sensor is equal to or less than the load ratio of the sixth sensor, the left foot is normal and the right foot arch is formed. It is evaluated as insufficient (step S329), and in this case, it is determined that the right foot may be a flat foot (step S330). When the load ratio of the No. 2 sensor is larger than the load ratio of the No. 6 sensor, it is evaluated as normal for both feet (step S331).

Further, in the comparison (step S328) after the foot arch evaluation unit 514 evaluates that the left foot arch is insufficiently formed (step S324), when the load ratio of the second sensor is larger than the load ratio of the sixth sensor, the left foot It is evaluated that the right foot is normal due to insufficient arch formation (step S332), and in this case, it is determined that the left foot may be a flat foot (step S333). Further, when the load ratio of the No. 2 sensor is equal to or less than the load ratio of the No. 6 sensor, it is evaluated that the arches of both feet are insufficiently formed (step S334), and it is determined that there is a possibility of both feet flat feet (step S335). ).

<Experimental results>
As a result of evaluating flat feet for 724 people by the foot arch evaluation unit 514, it was possible to evaluate with an accuracy of 91.5%.

<Processing by Hallux Grounding Evaluation Unit 517>
FIG. 28 is a flowchart showing a processing example of the hallux grounding evaluation unit 517. In FIG. 28, the toe ground contact evaluation unit 517 refers to the left foot 4th sensor: forefoot 2 maximum pressure value average and the right foot 4th sensor: forefoot 2 maximum pressure value average from the walking processing data 527 (step S341). ).

Next, the hallux ground contact evaluation unit 517 evaluates the ground contact of the hallux based on the total value obtained by summing the pressure values indicated by both sensors (step S342). For example, the evaluation is performed by comparing the total value with a predetermined value set in advance. Hereinafter, in the description, it is assumed that the predetermined value is 5N (Newton). However, the predetermined value is a value that can be set, and is, for example, a value determined by body weight or the like. When the predetermined value is "5N", the body weight is about "40 kg to 50 kg". Further, the predetermined value may be "50 kPa". That is, the evaluation may be performed by either force or pressure.

For example, when it is evaluated that the total value of the pressure values indicated by both sensors is 5N or more, the hallux grounding evaluation unit 517 evaluates that the hallux grounding is normal (step S343).

Further, when it is evaluated that the total value of the pressure values indicated by both sensors is less than 5N and larger than 0N, the hallux ground contact evaluation unit 517 evaluates that the ground contact of the hallux is weak and the finger is slightly floating (step S344). ).

Further, when the total value of the pressure values indicated by both sensors is 0N, that is, when it is evaluated that there is no output, the hallux grounding evaluation unit 517 evaluates that the hallux is not grounded and is a floating finger (step S345). ).

As described above, the hallux grounding evaluation unit 517 outputs the evaluation result of the hallux grounding.

<Processing by Comprehensive Evaluation Department 515>
The comprehensive evaluation unit 515 includes a forefoot local load evaluation unit 508, an internal / external foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a double leg support ratio evaluation unit 512, and a laterality evaluation unit 513. , The evaluation result by the foot arch evaluation unit 514, the evaluation result by the mother toe contact evaluation unit 517, and the life log data 524 are referred to, and a comprehensive evaluation is performed for each user.

FIG. 29 is a flowchart showing a processing example of the comprehensive evaluation unit 515. In FIG. 29, the comprehensive evaluation unit 515 calculates the body balance degree based on the evaluation result of the multi-step balance evaluation unit 510 and the evaluation result of the left-right difference evaluation unit 513 (step S41). Specifically, by adding, multiplying, standardizing, etc. of each evaluation result (if it is not quantified, it is quantified), it is finally scored to, for example, 1 to 10 to improve the body balance. calculate.

Next, the comprehensive evaluation unit 515 calculates the walking rhythm (degree) based on the evaluation result of the standing time evaluation unit 511 and the evaluation result of the both leg support ratio evaluation unit 512 (step S42). Specifically, by adding, multiplying, standardizing, etc. of each evaluation result (if it is not quantified, it is quantified), it is finally scored to a value of 1 to 10, for example, to improve the walking rhythm. calculate.

Next, the comprehensive evaluation unit 515 has a beautiful foot based on the evaluation result of the forefoot local load evaluation unit 508, the evaluation result of the supination / supination foot evaluation unit 509, and the evaluation result of the foot arch evaluation unit 514. The degree is calculated (step S43). Specifically, by adding, multiplying, standardizing, etc. of each evaluation result (if it is not quantified, it is quantified), it is finally scored to a value of 1 to 10, for example, to improve the degree of beauty. calculate. It should be noted that the degree of similarity when compared with a general healthy foot and whether or not an anatomically abnormal load is applied may be taken into consideration in the calculation of the degree of beautiful foot. In addition, the degree of beautiful legs is a value that increases when the legs are in a healthy state.

The degree of beautiful foot is calculated using at least one of the callus / corn evaluation result, the pronation / supination foot evaluation result, the foot arch evaluation result, and the mother toe contact evaluation result. .. Then, the calculation may weight the evaluation result. That is, the evaluation result of the hallux contact may be added to the calculation of the degree of beautiful foot.

In addition, the foot arch evaluation result has a larger weight as a value indicating a health condition as compared with other evaluation results. Therefore, the degree of beauty may be calculated only by the foot arch evaluation result, or may be calculated by increasing the weight of the foot arch evaluation result. With such a calculation method, the degree of beauty can be calculated with high accuracy.

Next, the comprehensive evaluation unit 515 refers to the life log data 524 and calculates the activity level (step S44). Specifically, the activity is calculated by finally scoring a value of, for example, 1 to 10 by adding / multiplying / standardizing the distance traveled, the number of steps, the average walking speed, etc. within the latest predetermined period.

The calculated comprehensive evaluation results (beautiful legs, body balance, walking rhythm, activity) can be displayed (visualized) by plotting on a graph (radar chart) as shown in FIG. 30.

<Processing by Comprehensive Analysis Department 516>
The comprehensive analysis unit 516 performs a comprehensive analysis of walking motion / activities of daily living data based on the data obtained by the above-mentioned processing and other additional data. Additional data includes user data 522, shoe data 523, and life log data 524. The "daily schedule", "destination", etc. are used by manually inputting the user and acquiring the information recorded in the Internet service having an arbitrary schedule function or calendar function used by the user. "Movement distance", "number of steps", "average walking speed", "maximum position information (GPS)", etc. are acquired from the information terminal 3 or any wearable information terminal used by the user, and are calculated and recorded from the acquired information. Information such as the "shoe maker model number" is acquired by the user manually inputting or reading the shoe product barcode. In addition, one week to one month after purchasing the shoes, information such as "whether or not the shoes are rubbed", "whether or not the shoes are rubbed", "whether or not the callus is present", "whether or not the callus is present", "whether or not there is pain", "presence or absence of pain", "presence or absence of compression", "presence or absence of compression", etc. Entered.

As an example of analysis, the comprehensive analysis unit 516 shows how another person who has a foot condition similar to the current foot condition of the user of this system and has a similar amount of activity can perform at a time after the similar time. It is possible to identify whether or not the foot state has changed and make a prediction judgment of the future foot state.

[Specific example of analysis # 1: Comparison with others]
The processing up to the comprehensive evaluation by the comprehensive evaluation unit 515 is performed on the accumulated post-walking data 527 of all users, and the comprehensive analysis unit 516 can divide the groups of similar tendencies by performing clustering. The comprehensive analysis unit 516 can show the result to the user of this system as the ratio (small number) to the total of the group to which the system belongs and the distance to the comprehensive evaluation index distribution of the good group. A radar chart as shown in FIG. 30 can be used for this feedback.

[Specific example of analysis # 2: Recommendation of shoes]
The comprehensive analysis unit 516 presents suitable shoe candidates to the user, together with feedback of other analysis results or according to individual requests.

About 70% of adult women regret choosing shoes, and one of the factors is that shoes and footwear are selected based on individual subjectivity and experience. In addition, although the foot shape is temporarily measured at a shoe store, shoes are not selected based on walking data during daily activities. Here, based on the characteristics of the walking / foot condition of an individual evaluated based on objective and quantitative walking movement data, the other person is in a similar state by comparison with the other person, and then the disability, etc. By proposing shoes that have been worn since the time when the owners of healthy feet who did not have the problem were similar, we are trying to support the selection of appropriate shoes.

FIG. 31 is a flowchart showing a recommended processing example of shoes. In FIG. 31, the comprehensive analysis unit 516 extracts other users who have similar walking / stationary standing states to the target user (step S51). For the similarity in this case, the information of "foot length", "foot width", "foot height", and "foot circumference" may be extracted from the user data 522 to see the similarity of the outer shape of the foot, or the data after walking processing 527 and the stationary standing. The similarity of each parameter may be seen from the post-position processing data 528.

Next, the comprehensive analysis unit 516 narrows down to other users whose evaluation results of the evaluation unit 507 or the comprehensive evaluation unit 515 are similar (step S52).

Next, the comprehensive analysis unit 516 narrows down the other users who have not been evaluated for the disability since the time when they are considered to be similar (step S53).

Next, the comprehensive analysis unit 516 acquires shoe data 523 that has been used since the time when it is considered to be similar to the narrowed down other users, and presents it as a recommended shoe selection candidate (step S54).

The comprehensive analysis unit 516 acquires information such as the gender, age, and generation of the target user and other users from the user data 522, and further acquires various information indicating the behavior pattern from the life log data 524, and these are used. It may be possible to further narrow down to other similar users.

Although the processing example is for the case where the shoe candidate is presented, the auxiliary equipment such as the insole, the toe correction pad, and the bunion supporter may be presented in the same manner.

[Specific example of analysis # 3: Prediction of foot abnormalities due to the use of shoes]
The comprehensive analysis unit 516 predicts foot abnormalities due to the use of shoes by, for example, the following procedure.

1. 1. Information on "foot length", "foot width", "foot height", and "foot circumference" is extracted from the user data 522, and another person having a similar outer shape of the foot is identified by comparison among others.

2. Above 1. From the processed data of others identified in (including all time series data from the past data to the time of use by the user of this system), using the shoe data 523, the shoes selected by the user of this system The post-walking data 527 and the static standing data 528 acquired while using similar shoes are identified.

3. 3. Above 2. From the processed data specified in step 1, select data whose walking function is similar to that of the user of this system. The processing content will be described later.

4. If there is similar applicable data, information about the foot and shoes when the corresponding user (user group) used similar shoes (specifically, "callus formation", "presence or absence of shoe rubbing", "shoes" By capturing the information on "compression" and "pain"), it is determined that there is a possibility or no foot abnormality when the user of this system continues to use the shoes selected. In this case, the possibility of "presence or absence of callus formation", "presence or absence of shoe rubbing", "presence or absence of pressure", and "presence or absence of pain" can be predicted, and it is possible to predict over time from the start of wearing to the occurrence. Become.

FIG. 32 is a flowchart showing a processing example of determining target data having a similar walking function in the above processing “3.”. In FIG. 32, the comprehensive analysis unit 516 determines the average maximum pressure value of the sensors 3, 4, 5, and 7 of both feet and the COP bending angle according to the data 527 after walking processing for the user A of this system and the user group B of another person. Is extracted and referenced (step S61).

Next, the comprehensive analysis unit 516 identifies the area at the time of getting out of bed from the average of the maximum pressure values of the sensors 3, 4, 5, and 7 of both feet (step S62).

Next, the comprehensive analysis unit 516 extracts an object whose area at the time of getting out of bed coincides with that of A from group B (step S63). If the areas when getting out of bed do not match, the process ends.

Next, the comprehensive analysis unit 516 sets the walking type A when the COP bending angle is larger than 175, the walking type B when the COP bending angle is smaller than 155, and the walking type C in other cases (step S64).

Next, the comprehensive analysis unit 516 extracts an object whose walking type (A to C) matches A from the group B (step S65), and determines a comparison object with respect to A (step S66). If the walking types do not match, the process ends. In this case, users having data belonging to the same walking type can be regarded as having similar walking functions.

<Summary>
As described above, according to the present embodiment, changes over time in foot / walking data of users in daily life can be captured, data of a plurality of users can be uniformly collected, and appropriate analysis / evaluation can be performed. Will be done. More specifically
・ It is possible to evaluate and predict not only the foot condition and walking function within an individual, but also the comparison between individuals including data from others.
・ It is possible to optimize walking (optimization of foot pain, etc.) by predicting signs of disease or disability.
・ It leads to the user's own health management and shoe selection in daily life.
・ Since all age groups, from children to the elderly, are targeted, it is possible to analyze aging data as a record of child development and growth.

The present invention has been described above according to a preferred embodiment of the present invention. Although the present invention has been described here with reference to specific examples, various modifications and modifications may be made to these specific examples without departing from the broad purpose and scope of the invention defined in the claims. It is clear that you can. That is, it should not be construed that the present invention is limited by the details of the specific examples and the accompanying drawings.

This international application claims priority based on Japanese Patent Application No. 2017-044385 filed on March 8, 2017, the entire contents of which are incorporated into this international application.

1 Shoes 2 Measuring device 21 Sensor unit 211 Base material 212 Pressure sensor 22 Communication unit 3 Information terminal 4 Network 5 Server device 501 Basic data input unit 502 Measurement data receiving unit 503 Data analysis unit 504 Standing period data analysis unit 505 Static standing data Analysis unit 506 Life log writing unit 507 Evaluation unit 508 Forefoot local load evaluation unit 509 Inward / external foot evaluation unit 510 Multi-step balance evaluation unit 511 Standing time evaluation unit 512 Both leg support ratio evaluation unit 513 Left-right difference evaluation unit 514 Foot arch evaluation department 515 Comprehensive evaluation department 516 Comprehensive analysis department 517 Mother toe ground contact evaluation department 521 Database 522 User data 523 Shoe data 524 Life log data 525 Position data 526 Measurement data 527 Post-walking data 528 Post-stationary data 6 Management terminal

Claims (16)

A data acquisition unit that acquires measurement data of at least the sole pressure for a predetermined time during walking and standing still from one or more sensors provided on the insoles of shoes used by a plurality of users.
By analyzing the measurement data, for each user, at least plantar pressure parameters, to generate a gait processed data including the foot pressure center parameter showing the bending angle of the foot pressure center path, and time parameters accumulated during the walk Analysis department and
An evaluation unit that evaluates the supination and supination legs based on the post-walking data.
A walking / foot evaluation device. The analysis unit analyzes the measurement data in a state where the standing posture is maintained for a certain period of time among the measurement data, and performs a static standing process including at least a plantar pressure parameter and a foot pressure center parameter during a static standing position. Post-data is further generated and accumulated,
The walking / foot evaluation device according to claim 1, wherein the evaluation unit further evaluates the foot arch or the laterality of the foot based on the data after the static standing process. The analysis unit acquires the average value of a plurality of steps of the maximum pressure value of each sensor from the measurement data of the sole pressure at a predetermined time during walking and uses it as the sole pressure parameter during walking.
The walking / foot evaluation device according to claim 1 or 2, wherein the evaluation unit further evaluates the ground contact of the mother toe based on the data after the walking process. The walking / foot evaluation device according to any one of claims 1 to 3, wherein the evaluation unit further evaluates the local weighting of the forefoot based on the post-walking data. The walking / foot evaluation device according to claim 4, wherein the evaluation unit evaluates the callus / corn based on the evaluation result of the local weighting of the forefoot. The walking / foot evaluation device according to any one of claims 1 to 5, wherein the evaluation unit further evaluates the balance of a plurality of steps based on the data after the walking process. The analysis unit acquires the stance time, the support ratio of both legs, and the support ratio of the single leg from the measurement data of the sole pressure at a predetermined time during walking, and uses them as the time parameters.
The walking / foot evaluation device according to any one of claims 1 to 6, wherein the evaluation unit further evaluates the standing time or the support ratio of both legs based on the data after the walking process. The walking / foot evaluation device according to claim 2, further comprising a comprehensive evaluation unit that performs comprehensive evaluation for each user based on the evaluation result by the evaluation unit. The evaluation unit evaluates the local weight of the forefoot and the supination and supination based on the data after the walking process, and evaluates the foot part based on the data after the stationary standing process. Evaluate the arch and
8. The comprehensive evaluation unit calculates the degree of beautiful foot based on the evaluation result of the local weighting of the forefoot portion, the evaluation result of the supination foot and the supination side, and the evaluation result of the foot arch. The walking / foot evaluation device described in. The evaluation unit evaluates the balance of a plurality of steps based on the data after the walking process, and evaluates the laterality of the foot based on the data after the static standing process.
The walking / foot evaluation device according to claim 8 or 9, wherein the comprehensive evaluation unit calculates a body balance degree based on the evaluation result of the plurality of steps balance and the evaluation result of the laterality of the foot. The evaluation unit evaluates the standing time and the support ratio of both legs based on the data after the walking process.
The walking / foot evaluation device according to any one of claims 8 to 10, wherein the comprehensive evaluation unit calculates a walking rhythm based on the evaluation result of the standing time and the evaluation result of the support ratio of both legs. .. The comprehensive evaluation department
Among the evaluation results by the evaluation unit, at least one evaluation result is similar, and another user who has no abnormality in the foot is identified.
The walking / foot evaluation device according to any one of claims 8 to 11, which presents shoes used by the other user as selection candidates. The comprehensive evaluation department
Based on the post-walking data and / or the post-standing static data accumulated by the analysis unit, a comprehensive analysis is performed in consideration of temporal state changes, and early detection or prediction warning of foot abnormality is performed. The walking / foot evaluation device according to any one of claims 8 to 12, wherein the walking / foot evaluation device is characterized. Data acquisition process that acquires measurement data of at least sole pressure for a predetermined time during walking and standing still from one or more sensors provided on the insoles of shoes used by a plurality of users.
By analyzing the measurement data, for each user, at least plantar pressure parameters, to generate a gait processed data including the foot pressure center parameter showing the bending angle of the foot pressure center path, and time parameters accumulated during the walk Analysis processing and
An evaluation process that evaluates the supination and supination legs based on the post-walking data.
Causing a computer to execute the, walking-foot evaluation program. The computer provided in the walking / foot evaluation device
Data acquisition process that acquires measurement data of at least sole pressure for a predetermined time during walking and standing still from one or more sensors provided on the insoles of shoes used by a plurality of users.
By analyzing the measurement data, for each user, at least plantar pressure parameters, to generate a gait processed data including the foot pressure center parameter showing the bending angle of the foot pressure center path, and time parameters accumulated during the walk Analysis processing and
An evaluation process that evaluates the supination and supination legs based on the post-walking data.
To run, a method of operating a walk-foot evaluation device. A data acquisition unit that acquires measurement data of at least the sole pressure for a predetermined time during walking and standing still from one or more sensors provided on the insoles of shoes used by a plurality of users.
The measurement data is analyzed to generate and accumulate post-walking data including at least the sole pressure parameter during walking, the foot pressure center parameter indicating the flexion angle of the foot pressure center locus, and the time parameter for each user. Analysis department and
An evaluation unit that evaluates the supination and supination legs based on the post-walking data.
A walking / foot evaluation system.

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