JP2021119935A - Walking analysis method and walking analysis device - Google Patents

Abstract

To provide a walking analysis method which can evaluate the walking state in more detail.SOLUTION: A walking analysis method for analyzing the walking state on the basis of measurement information about the pressure applied to the sole measured in walking comprises: a division step S13 of dividing a one-step section A from the landing on the ground to the separation from the ground of the foot in walking into a plurality of small sections Ah, Am, At; a coordinate calculation step S16 of calculating the two-dimensional coordinates of the foot pressure center of the sole for each constant time in the one-step section A; and a deflection value calculation step S18 of calculating deflection values Sh, Sm, St in a small section unit.SELECTED DRAWING: Figure 12

Description

The present invention relates to a gait analysis method and a gait analyzer.

Conventionally, the pressure applied to the sole of a foot during human walking is measured, and the feature amount related to the walking state is acquired from the obtained measurement information, and the walking state is evaluated based on the acquired feature amount. It has been.

For example, in the gait analysis method disclosed in Patent Document 1, the sole pressure parameter, the foot pressure center parameter, and the time parameter are acquired based on the pressure applied to the sole of the foot measured during walking. Then, the COP flexion angle, which is the flexion angle of the foot pressure center locus from the landing of one foot to the takeoff, is calculated from each of the above parameters.

International Publication No. 2018/164157

There was room for improvement in the feature amount and the calculation method of the feature amount disclosed in Patent Document 1 in order to evaluate the walking state of the subject in more detail.
The present invention has been made in view of such circumstances, and an object of the present invention is to provide a gait analysis method and a gait analyzer capable of evaluating a gait state in more detail.

The gait analysis method that solves the above problems is a gait analysis method that analyzes the walking state based on the measurement information on the pressure applied to the sole of the foot measured during walking, and the measurement information is the portion applied to the sole of one foot. Including the measured values of the pressure measured over time, the division step that divides the step section from the landing of the foot to the takeoff during walking into a plurality of subsections, and the sole of the foot at regular intervals in the subsection. In the coordinate calculation step for calculating the two-dimensional coordinates of the center of foot pressure, and within the same subsection, the three consecutive two-dimensional coordinates at regular time intervals are set to coordinates P _n , coordinates P _{n + 1} , and coordinates P _{n + 2} in order. when a deflection value calculation step of calculating a sum which is deflection value of the vector and deflection formed by angle indicating the amount of change from the vector and the coordinate P _{n + 1} indicating the amount of change from the coordinate P _n to the coordinate P _{n + 1} to the coordinate P _{n + 2} To be equipped.

According to the above configuration, the step section from the landing of the foot to the takeoff during walking is divided into a plurality of subsections, and the runout value of each subsection is calculated. The above-mentioned runout value is a value that quantifies how much the center of foot pressure swings, and by checking the runout value, the smoothness of the movement of the center of gravity can be quantitatively determined. In particular, by using the runout value subdivided into small section units that divide the step section instead of the runout value of the entire step section, more detailed analysis such as when the center of foot pressure swings becomes possible. .. Therefore, the walking state of the subject can be evaluated in more detail by analyzing the walking state of the subject using the runout value in units of small sections obtained by the above configuration.

The plurality of small sections are preferably a heel section in which pressure is applied to the heel side, a toe section in which pressure is applied to the toe side, and an intermediate section excluding the heel section and the toe section in the one-step section.

According to the above configuration, the heel section where the pressure is applied to the heel side and the toe section where the pressure is applied to the toe side can be set more accurately, and the accuracy of the runout value as the subdivided feature amount is improved.

The measurement information is the first position and the second position separated in the front-rear direction, and the third position and the fourth position separated in the left-right direction across the line connecting the first position and the second position on the sole of the foot. It is preferable to include a detection value obtained by detecting the pressure of each part over time.

According to the above configuration, the two-dimensional coordinates of the center of foot pressure can be acquired more accurately, and the accuracy of the runout value acquired from the two-dimensional coordinates of the center of foot pressure is improved.
A classification in which a target group for which the feature amount is obtained is clustered into a plurality of subsets based on the feature amount, using the runout value, the maximum pressure value in the small section, and the section time of the small section as feature amounts. It is preferable to have a step.

According to the above configuration, by confirming each classified subset, characteristic data in the target group, that is, a subject in a characteristic walking state can be easily confirmed.
The gait analyzer that solves the above problems is a gait analyzer that analyzes the gait state based on the measurement information on the pressure applied to the sole of the foot measured during walking, and obtains the feature amount related to the gait state from the measurement information. The information processing unit is provided with an information processing unit to be acquired, and the measurement information includes a measured value obtained by measuring the pressure of each part applied to the sole of one foot over time. In the same subsection, a division part that divides the one-step section of the above into a plurality of subsections and a coordinate calculation part that calculates the two-dimensional coordinates of the foot pressure center of the sole of the foot at regular intervals in the subsection are constant. turn coordinate _{P n} three of the two-dimensional coordinates of consecutive time intervals, the coordinate _{P n + 1,} when the coordinates _{P n + 2,} coordinates from the vector and the coordinate _{P n + 1} indicating the amount of change from the coordinate _{P n} to the coordinate _{P n + 1 P n + 2} It is provided with a runout value calculation unit that calculates a runout value that is the sum of the runout angles formed by a vector indicating the amount of change to.

The information processing unit includes a maximum pressure value acquisition unit that acquires the maximum pressure value of the small section, a section time acquisition unit that acquires the section time of the small section, a runout value, and a maximum pressure value of the small section. It is preferable to provide a classification unit for clustering the target group from which the feature amount is obtained into a plurality of subsets based on the feature amount, with the section time of the small section as the feature amount.

According to the present invention, the walking state can be evaluated in more detail.

Explanatory drawing of a measuring device and a gait analyzer. Explanatory drawing of arrangement of a pressure sensor. Explanatory drawing of information processing department. Explanatory drawing of feature data. Graph of waveform data. Graph of waveform data of one step section. A graph of two-dimensional coordinates of the center of foot pressure. Explanatory drawing of the runout angle. A graph of waveform data related to feature data classified into group 0 and a graph of two-dimensional coordinates of the center of foot pressure. A graph of waveform data related to feature data classified into group 1 and a graph of two-dimensional coordinates of the center of foot pressure. A graph of waveform data related to feature data classified into group 2 and a graph of two-dimensional coordinates of the center of foot pressure. Flowchart of gait analysis method.

Hereinafter, an embodiment of the present invention will be described.
First, the measuring device 10 and the walking analysis device 20 used in the walking analysis method of the present embodiment will be described.

As shown in FIGS. 1 and 2, the measuring device 10 includes a base 11 used as an insole for shoes. Four pressure sensors, a heel sensor 12a, a toe sensor 12b, an inner sensor 12c, and an outer sensor 12d, are attached to the base 11 as pressure sensors 12 for detecting the pressure of each part applied to the sole of the foot during walking. There is.

As shown in FIG. 2, at the base 11, the heel sensor 12a is arranged at a portion where the load of the heel is applied, and detects the pressure applied to the heel of the sole of the foot. The toe sensor 12b is arranged at any of the toe-loaded portions of the 2nd to 4th toes and detects the pressure applied to the toes of the sole of the foot.

The inner sensor 12c is located inside the line L connecting the heel sensor 12a and the toe sensor 12b and is arranged at a portion where the load of the ball of the finger is applied, and detects the pressure applied to the inner portion of the sole of the foot. The outer sensor 12d is located outside the line L and is located at a portion where the load of the hypothenar eminence is applied, and detects the pressure applied to the outer portion of the sole of the foot.

In other words, the heel sensor 12a and the toe sensor 12b are arranged so as to detect the pressure at the first position and the second position which are separated from each other in the front-rear direction on the sole of the foot. The inner sensor 12c and the outer sensor 12d are arranged so as to detect the pressure at the third position and the fourth position separated in the left-right direction across the line L connecting the first position and the second position.

Each pressure sensor 12 independently detects the pressure applied to the sole of the foot at predetermined time intervals. The predetermined time is, for example, 5 to 30 milliseconds. In this embodiment, the pressure is set to be detected every 20 milliseconds.

As the pressure sensor 12, a known pressure sensor using a piezoelectric element or the like can be used. In particular, in view of the usage situation in which the sensor is arranged on the sole of the foot, it is preferable to use an elastomer-made capacitance type sensor using a dielectric elastomer from the viewpoint of elasticity and durability. Examples of the dielectric elastomer include crosslinked polyrotaxane, silicone elastomer, acrylic elastomer, and urethane elastomer.

As shown in FIG. 1, the measuring device 10 is equipped with a transmission unit 13 that transmits each detected value detected by each pressure sensor 12 to the gait analyzer 20. Each detected value detected by each pressure sensor 12 is a detected value that can be converted into a pressure value according to the detection method of the pressure sensor such as a capacitance value and an electric resistance value. Therefore, the "detected value" described below can be read as the measured value of the pressure at the position where the pressure sensor 12 on the sole of the foot is attached.

The measuring device 10 is prepared for both the left foot and the right foot, and if necessary, only one of the left and right or both left and right can be used.
As shown in FIG. 1, the gait analyzer 20 has a receiving unit 21 that receives measurement information transmitted from a transmitting unit 13 of the measuring device 10, and information that acquires a feature amount related to a walking state from the received measurement information. It includes a processing unit 22, a display unit 23 that displays a feature amount acquired by the information processing unit 22, and an input unit 24 that inputs various information.

As the gait analyzer 20, for example, a personal computer such as a mobile terminal or a tablet terminal can be used. As the display unit 23, for example, a known display device such as a liquid crystal display can be used. As the input unit 24, for example, a known input device such as a keyboard, a mouse, or a touch panel can be used. The receiving unit 21 of the gait analyzer 20 has a wired or wireless communication means, and communicates with the transmitting unit 13 of the measuring device 10 by a known communication method.

As shown in FIG. 3, the information processing unit 22 includes a storage unit 30, a waveform data creation unit 31, a division unit 32, a section maximum value acquisition unit 33, a section time acquisition unit 34, a coordinate calculation unit 35, and a stay ratio calculation unit 36. , A runout value calculation unit 37, a classification unit 38, and a display processing unit 39.

The storage unit 30 is configured to store the measurement information transmitted from the measuring device 10 and to store the feature amount data based on the measurement information. As shown in FIG. 4, the feature amount data includes identification information such as a date and time and a user name, and feature amounts F1 to F11 related to a walking state. Further, the storage unit 30 stores a plurality of feature data such as past feature data of the same subject, feature data of different subjects, and model data set by an expert such as a medical worker. Examples of the model data include feature data obtained from measurement results of subjects having specific symptoms such as clubfoot and clubfoot. Further, the storage unit 30 stores an execution program for controlling the execution of each process in the information processing unit 22.

The waveform data creation unit 31 is the time of the detected value for each part of the sole of one foot (for example, the left foot) detected by each pressure sensor 12 based on the measurement information to be analyzed stored in the storage unit 30. Create waveform data that represents the change. FIG. 5 shows an example of waveform data created by the waveform data creation unit 31. In FIG. 5, the solid line indicates the detection value of the heel sensor 12a, the alternate long and short dash line indicates the detection value of the toe sensor 12b, the alternate long and short dash line indicates the detection value of the inner sensor 12c, and the broken line indicates the detection value of the outer sensor 12d. Indicates the detected value.

From the created waveform data, the division unit 32 extracts a section for a specific step from the landing of the foot during walking to the takeoff as a target section, and further divides the extracted target section into a plurality of subsections. ..

As shown in FIG. 5, the waveform data during walking is a waveform that periodically repeats a section in which the pressure value is substantially constant and a section in which the detected value changes. In the waveform data during walking, the section where the detected value is substantially constant is the section where the foot is away from the ground, and the section where the detected value is changed is the section where the foot is in contact with the ground. The dividing portion 32 has a starting point t1 at which the heel pressure value starts to rise from a section where the detected value of each part is substantially constant, and is the most of the starting points where the detected value of each part is substantially constant. A specific step section A is extracted from the step section A with the late start point as the end point t2.

The one-step section A to be extracted can be arbitrarily selected by the operator using the gait analyzer 20, or can be selected by the division unit 32 based on a preset standard. As the above criteria, for example, one step section A located in a preset order such as the tenth step from the start of measurement may be selected, or may be randomly selected.

As shown in FIG. 6, the dividing section 32 divides the extracted step section A into a small section of a heel section Ah, a toe section At, and an intermediate section Am.
The heel section Ah is an initial small section of the step section A in which pressure is relatively applied to the heel side. In the present embodiment, the section from the start point t1 of the step section A to the time point t3 where the detection value of the heel sensor 12a reaches the peak is defined as the heel section Ah.

The toe section At is a small section at the end of the one-step section A in which pressure is relatively applied to the toe side. In the present embodiment, the section from the time point t4 where the detected value of the toe sensor 12b is the largest among the detected values of the four pressure sensors 12 to the end point t2 of the step section A is defined as the toe section At. Depending on how you walk, there may be no toe section At.

The intermediate section Am is a small section obtained by excluding the heel section Ah and the toe section At from the step section A. If there is a toe section At, the section from the time point t3 to the time point t4 is set as the intermediate section Am, and if there is no toe section At, the section from the time point t3 to the time point t2 is set as the intermediate section Am.

The section maximum value acquisition unit 33 includes the maximum value of the detected values of the four pressure sensors 12 in the heel section Ah (hereinafter, referred to as the heel section maximum value Ah _max ), and the four pressure sensors 12 in the toe section At. The maximum value of the detected value (hereinafter, referred to as the toe section maximum value At _max ) is acquired from the measurement information. Then, the acquired heel section maximum value Ah _max and the toe section maximum value At _max are stored in the storage unit 30 as the feature amounts F1 and F2 constituting the feature amount data shown in FIG.

The section time acquisition unit 34 acquires the section time Th, which is the length of the heel section Ah, the section time Tm, which is the length of the intermediate section Am, and the section time Tt, which is the length of the toe section At, from the measurement information. Then, the acquired section times Th, Tm, and Tt are stored in the storage unit 30 as feature quantities F3 to F5 constituting the feature quantity data shown in FIG. In addition, each section time may be the time itself such as the number of seconds, or may be a parameter corresponding to the section time such as the number of measurement points in the corresponding section.

As shown in FIG. 7, the coordinate calculation unit 35 is two-dimensional of the foot pressure center for each detection time in each subsection of the heel section Ah, the toe section At, and the intermediate section Am based on the measurement information of the step section A. The coordinates (X (t), Y (t)) are calculated. X (t) is the left-right coordinate of the foot pressure center at time t, and Y (t) is the front-back coordinate of the foot pressure center at time t. The two-dimensional coordinates of the center of foot pressure can be obtained from the detected values of the four pressure sensors 12 detected at the same detection time. The points 12a to 12d in the two-dimensional coordinates shown in FIG. 7 indicate the positions of the pressure sensors 12 having the corresponding symbols.

As shown in FIG. 7, the stay ratio calculation unit 36 divides the two-dimensional coordinate system at the center of foot pressure into three preset regions B1, B2, and B3. Then, the stay ratio calculation unit 36 has a stay ratio Rh1 and a time to stay in the area B2, which is a ratio of the time to stay in the area B1 in the heel section Ah, based on the two-dimensional coordinates calculated by the coordinate calculation unit 35. The stay ratio Rh2, which is the ratio of the above, and the stay ratio Rh3, which is the ratio of the time spent in the area B3, are calculated. Then, the calculated stay ratios Rh1, Rh2, and Rh3 are stored in the storage unit 30 as the feature amount F6 constituting the feature amount data shown in FIG.

Similarly, in the intermediate section Am, the stay ratio calculation unit 36 has a stay ratio Rm1 which is a ratio of the time spent in the area B1, a stay ratio Rm2 which is a ratio of the time stayed in the area B2, and a time spent in the area B3. The stay ratio Rm3, which is the ratio of Then, the calculated stay ratios Rm1, Rm2, and Rm3 are stored in the storage unit 30 as the feature amount F7 constituting the feature amount data shown in FIG.

Similarly, in the toe section At, the stay ratio calculation unit 36 has a stay ratio Rt1 which is a ratio of the time spent in the area B1, a stay ratio Rt2 which is a ratio of the time stayed in the area B2, and a time spent in the area B3. The stay ratio Rt3, which is the ratio of, is calculated. Then, the calculated stay ratios Rt1, Rt2, and Rt3 are stored in the storage unit 30 as the feature amount F8 constituting the feature amount data shown in FIG.

Each of the above stay ratios may be a numerical value based on the stay time itself such as the number of seconds spent in the corresponding area, or corresponds to the stay time such as the number of measurement points located in the area in the two-dimensional coordinate system. It may be a numerical value based on the parameters to be used.

Based on the two-dimensional coordinates calculated by the coordinate calculation unit 35, the runout value calculation unit 37 sets the runout value Sh of the heel section Ah and the runout value Sm of the intermediate section Am as runout values indicating the runout of the center of foot pressure. The runout value St of the toe section At is calculated. The runout value Sh of the heel section Ah is calculated as follows.

As shown in FIG. 8, in the heel section Ah, the two-dimensional coordinates of the foot pressure center at three consecutive measurement points are coordinate P _n , coordinate P _{n + 1} , and coordinate P _{n + 2} , respectively, and coordinates P n to coordinates P _n. The angle formed by the vector V1 indicating the amount of change to _{n + 1} and the vector V2 indicating the amount of change from the coordinate P _{n + 1 to the coordinate P n + 2} is defined as the deflection angle θ.

The runout value calculation unit 37 calculates the runout angle θ when the _{above coordinates P n} are used for the two-dimensional coordinates of all foot pressure centers except the last two in the heel section Ah, and these runout angles θ. Is added up to calculate the runout value Sh, which is the sum of the runout angles θ. The runout value Sm of the intermediate section Am and the runout value St of the toe section At are also calculated in the same manner. Then, the runout value calculation unit 37 stores the calculated runout values Sh, Sm, and St in the storage unit 30 as feature amounts F9 to F11 constituting the feature amount data shown in FIG.

The classification unit 38 includes 11 feature quantities constituting the feature quantity data by targeting a plurality of sets of feature quantity data stored in the storage unit 30, including the feature quantity data consisting of newly acquired feature quantities. The target group is clustered into a plurality of subsets based on F1 to F11. Clustering is performed by dimensionally compressing feature data by principal component analysis and using a known method such as the k-means method or the GMM method.

FIGS. 9 to 11 show an example of the result of clustering a plurality of feature data by the k-means method. Here, clustering produces three subsets of group 0, group 1, and group 2. Then, three sets of waveform data of one step interval A and two-dimensional coordinate data of the center of foot pressure, which are the sources of the feature data classified into each group, are shown in FIGS. 9 to 11. 9-11 show the raw data after the measurement.

Group 0 shown in FIG. 9 is a group that can be labeled as having an average walking style. As a feature of group 0, the heel section Ah, the intermediate section Am, and the toe section At are clearly present in the step section A, and the load is transferred from the heel to the toe via the central part of the sole.

Group 1 shown in FIG. 10 is a group that can be labeled as a walking style that tends to walk on the outside. As a feature of Group 1, most of the step section A is occupied by the heel section Ah and the intermediate section Am, and the kicking motion with the toes is not included.

Group 2 shown in FIG. 11 is a group that can be labeled as a walking style that tends to walk in the inner thigh. As a feature of Group 2, most of the step section A is occupied by the heel section Ah and the toe section At, and the toes arrive early after the heel reaches the ground.

The display processing unit 39 is a group of the feature amount data shown in FIG. 4, the waveform data of the step section A shown in FIG. 6, the two-dimensional coordinate data of the foot pressure center shown in FIG. 7, and the feature amount data classified by the classification unit 38. Etc. are displayed on the display unit 23.

Next, the gait analysis method of the present embodiment will be described based on the flowchart shown in FIG.
First, before the gait analysis, as a measurement step, a gait test is performed by a subject wearing shoes in which the measuring device 10 is arranged on the sole. As a walking test, for example, walking on a flat surface for about 10 m can be mentioned. The detected value detected by each pressure sensor 12 of the measuring device 10 during the walking test is transmitted to the walking analyzer 20 as measurement information and stored in the storage unit 30 of the walking analyzer 20.

After the gait test, when an execution command by the operator is input to the gait analyzer 20, the waveform data creation unit 31 creates waveform data based on the measurement information stored in the storage unit 30 as the waveform creation step S11. .. Subsequently, the division unit 32 extracts a specific step section A from the created waveform data as the extraction step S12, and sets the step section A as the heel section Ah, the toe section At, and the intermediate section Am as the division step S13. Divide into small sections.

Subsequently, as the maximum pressure value acquisition step S14, the section maximum value acquisition unit 33 acquires the heel section maximum value Ah _max and the toe section maximum value At _max in the step section A, and constitutes a new feature amount data. It is stored in the storage unit 30 as F1 and F2.

Subsequently, as the section time acquisition step S15, the section time acquisition unit 34 acquires the section time Th of the heel section Ah, the section time Tm of the intermediate section Am, and the section time Tt of the toe section At in the step section A, and newly obtains the section time Tt. The feature amounts F3 to F5 that constitute the feature amount data are stored in the storage unit 30.

Subsequently, in the coordinate calculation step S16, the coordinate calculation unit 35 calculates the two-dimensional coordinates of the foot pressure center in the step section A. After that, as the stay ratio calculation step S17, the stay ratio calculation unit 36 sets the stay ratio Rh1, Rh2, Rh3 of the foot pressure center of the heel section Ah and the intermediate section Am based on the calculated two-dimensional coordinates of the foot pressure center. The stay ratio Rm1, Rm2, Rm3 at the center of the foot pressure and the stay ratio Rt1, Rt2, Rt3 at the center of the foot pressure in the toe section At are acquired, and the feature amounts F6 to F8 constituting the new feature amount data are stored in the storage unit 30. Remember.

Subsequently, in the runout value calculation step S18, the runout value calculation unit 37 calculates the runout values Sh, Sm, and St, and stores them in the storage unit 30 as feature amounts F9 to F11 constituting new feature amount data.

Subsequently, as the classification step S19, the classification unit 38 performs clustering using the new feature amount data and the feature amount data stored in the storage unit 30 as a target group, and classifies the feature amount data into a plurality of groups. ..

As a result of clustering, the operator can determine the walking state of the subject based on which group the feature data based on the current measurement information is classified. For example, as a result of clustering, when the group is already classified into a labeled group, the walking state of the subject can be determined based on the labeling. In addition, when it is classified into a group different from the previous analysis result based on the past measurement information, it can be judged that the walking method has changed.

Next, the operation of this embodiment will be described.
In the gait analysis method of the present embodiment, the step section A from the landing of the foot to the takeoff during walking is divided into three subsections of the heel section Ah, the intermediate section Am, and the toe section At, and each subsection is used. The runout values Sh, Sm, and St are calculated. The runout values Sh, Sm, and St are values that quantify how much the center of foot pressure is swinging at each timing of one step from landing to takeoff, and the runout values Sh, Sm, and St should be confirmed. Therefore, the smoothness of one step from landing to takeoff can be quantitatively judged. In particular, by setting the runout values Sh, Sm, and St subdivided into subdivision units in which the one-step section A is divided, it is possible to perform a more detailed analysis of when the center of foot pressure is swinging.

In addition, the swing of the center of foot pressure in each subsection tends to change according to the movement of the foot below the knee during walking. For example, when the subject has a tendency to walk outside or inside, it is easy to obtain characteristic runout values Sh, Sm, and St. Therefore, when clustering is performed using the runout values Sh, Sm, and St as feature quantities, it is possible to generate a group with a tendency to walk in the inner thigh and a group with a tendency to walk in the inner thigh as shown in FIGS. 9 to 11. Become. Therefore, the walking state of the subject can be evaluated in more detail by analyzing the walking state of the subject using the runout values Sh, Sm, and St in units of small sections obtained by dividing the step section A.

Next, the effect of this embodiment will be described.
(1) In the walking analysis method that analyzes the walking state based on the measurement information on the pressure applied to the sole of the foot measured during walking, the step section A from the landing of the foot to the takeoff during walking is divided into a plurality of small sections Ah, The division step S13 for dividing into Am and At, the coordinate calculation step S16 for calculating the two-dimensional coordinates of the foot pressure center of the sole at regular time intervals in the step section A, and the runout values Sh, Sm, St in small section units. It includes a runout value calculation step S18 to be calculated.

By analyzing the walking state of the subject using the runout values Sh, Sm, and St in small section units obtained by the above configuration, the walking state of the subject can be evaluated in more detail.
(2) In the division step S13, based on the relative relationship of the pressure applied to the sole of the foot, the step section A is divided into the heel section Ah in which the pressure is applied to the heel side and the toe section At in which the pressure is applied to the toe side. And the intermediate section Am excluding the heel section Ah and the toe section At in the one-step section A.

According to the above configuration, the heel section Ah in which pressure is applied to the heel side and the toe section At in which pressure is applied to the toe side can be set more accurately. Therefore, the accuracy of each feature amount such as the runout value obtained based on the divided subsections is improved.

(3) The measurement information includes the first position and the second position separated in the front-rear direction on the sole of the foot, and the third position separated in the left-right direction across the line L connecting the first position and the second position. The detection value obtained by detecting the pressure of each part of the fourth position over time is included.

According to the above configuration, the two-dimensional coordinates of the center of foot pressure can be obtained more accurately. As a result, the accuracy of the runout values Sh, Sm, and St obtained from the two-dimensional coordinates of the center of foot pressure is improved.
(4) With the runout values Sh, Sm, St, the heel section maximum value Ah _max , the toe section maximum value At _max , and the section time Th, Tm, Tt as feature quantities, the target group for which the feature quantity is obtained is selected as the feature quantity. A classification step S19 for clustering into a plurality of subsets based on the above is provided.

According to the above configuration, by confirming each classified subset, characteristic data in the target group, that is, a subject in a characteristic walking state can be easily confirmed.
(5) Stay ratio calculation in which the coordinate system of the two-dimensional coordinates of the foot pressure center is divided into a plurality of regions B1 to B3, and the stay ratio of the two-dimensional coordinates of the foot pressure center in each region B1 to B3 is calculated in small section units. The step S17 is provided.

The stay ratio of the two-dimensional coordinates of the foot pressure center in small section units obtained by the above configuration can be regarded as a value indicating the direction in which the foot pressure center swings at each timing of one step from landing to takeoff. Therefore, the walking state of the subject can be evaluated in more detail by analyzing the walking state of the subject by using the above-mentioned stay ratio together with the swing value of each small section.

In addition, this embodiment can be implemented by changing as follows. The present embodiment and the following modified examples can be implemented in combination with each other within a technically consistent range.
The number and arrangement of the pressure sensors 12 provided in the measuring device 10 are not limited to the configuration of the above embodiment, and may be appropriately changed within a range in which the two-dimensional coordinates of the center of foot pressure can be obtained. May be good.

The method of setting the start point t1 and the end point t2 of the one-step section A is not limited to the above embodiment. For example, the end point t2 may be a point at which a predetermined time has elapsed after the detected value of each portion becomes substantially constant.

The definitions of the heel section Ah, the intermediate section Am, and the toe section At are not limited to the above embodiments. For example, the section from the time when the detected value of the toe sensor 12b becomes the apex of the peak to the end point t2 of the step section A may be set as the toe section At. Further, the section from the start point t1 of the step section A to the end point t2 is set as the heel section Ah, and the section from before the specific time of the end point t2 of the step section A to the end point t2 is set as the toe section At. Each subsection may be set without using the relative pressure relationship. In these cases, the toe section At can be reliably provided.

-The method of dividing the one-step section A into a plurality of subsections is not limited to the above embodiment. For example, the intermediate section Am may be further divided into 4 or more small sections. It may be a small section divided independently of the section where pressure is applied to the heel side and the section where pressure is applied to the toe side.

-The method of dividing the two-dimensional coordinate system at the center of foot pressure when calculating the stay ratio is not limited to the above embodiment. For example, in the above embodiment, the region is divided into a plurality of regions arranged in the left-right direction, but the region may be divided into a plurality of regions arranged in the front-rear direction, or may be divided into a plurality of regions arranged in the front-rear and left-right directions. Further, the number of divisions of the two-dimensional coordinate system at the center of foot pressure may be 2 or 4 or more.

-In the above embodiment, the runout values of all the subsections have been acquired, but the configuration may be such that only the runout values of a specific subsection are acquired.
-Acquisition of features other than runout values is not essential and can be omitted if necessary.

-The type of the feature amount that constitutes the feature amount data for performing clustering is not limited to the above embodiment, and may include a runout value in units of small sections. For example, the feature amount data may consist only of the runout values Sh, Sm, St, the heel section maximum value Ah _max , the toe section maximum value At _max , and the section time Th, Tm, Tt, or the feature related to a specific small section. It may be feature data consisting only of quantities. Further, in addition to the feature amounts described in the above-described embodiment, the feature amount data may include other feature amounts. Examples of the other feature amount include the maximum value of the detected values of the four pressure sensors 12 in the intermediate section Am.

10 ... Measuring device 12 ... Pressure sensor 12a ... Heel sensor 12b ... Heel sensor 12c ... Inner sensor 12d ... Outer sensor 20 ... Walking analyzer

Claims (6)

It is a gait analysis method that analyzes the gait state based on the measurement information on the pressure applied to the sole of the foot measured during gait.
The measurement information includes a measured value obtained by measuring the pressure of each part applied to the sole of one foot over time.
A division step that divides the one-step section from the landing of the foot during walking to the takeoff into multiple subsections, and
A coordinate calculation step for calculating the two-dimensional coordinates of the foot pressure center of the sole at regular intervals in the small section, and
In the same within the sub-interval, in turn coordinate P _n three of the two-dimensional coordinates of consecutive predetermined time _intervals, the coordinate P _{n + 1,} when the coordinates P _{n + 2,} show a variation of the coordinate P _{n + 1} from coordinate P _n A gait analysis method comprising: a runout value calculation step for calculating a runout value, which is the sum of runout angles formed by a vector and a vector indicating the amount of change from coordinates P _{n + 1} to coordinates P _{n + 2.} In the division step, based on the relative relationship of pressure for each part applied to the sole of the foot, the step section is divided into a heel section in which pressure is applied to the heel side, a toe section in which pressure is applied to the toe side, and the step section. The gait analysis method according to claim 1, wherein the heel section and the intermediate section excluding the toe section are divided. The measurement information is the first position and the second position separated in the front-rear direction, and the third position and the fourth position separated in the left-right direction across the line connecting the first position and the second position on the sole of the foot. The gait analysis method according to claim 1 or 2, which includes a detected value obtained by detecting the pressure of each part over time. A classification in which a target group for which the feature amount is obtained is clustered into a plurality of subsets based on the feature amount, using the runout value, the maximum pressure value in the small section, and the section time of the small section as feature amounts. The gait analysis method according to any one of claims 1 to 3, further comprising a step. It is a gait analyzer that analyzes the gait state based on the measurement information on the pressure applied to the sole of the foot measured during gait.
It is equipped with an information processing unit that acquires features related to the walking state from the measurement information.
The measurement information includes a measured value obtained by measuring the pressure of each part applied to the sole of one foot over time.
The information processing unit
A division that divides the one-step section from the landing of the foot to the takeoff during walking into multiple subsections,
A coordinate calculation unit that calculates the two-dimensional coordinates of the foot pressure center of the sole of the foot at regular intervals in the small section, and
In the same within the sub-interval, in turn coordinate P _n three of the two-dimensional coordinates of consecutive predetermined time _intervals, the coordinate P _{n + 1,} when the coordinates P _{n + 2,} show a variation of the coordinate P _{n + 1} from coordinate P _n A gait analyzer characterized by comprising a runout value calculation unit that calculates a runout value that is the sum of runout angles formed by a vector and a vector indicating the amount of change from coordinates P _{n + 1} to coordinates P _{n + 2.} The information processing unit
The maximum pressure value acquisition unit that acquires the maximum pressure value in the small section, and
A section time acquisition unit that acquires the section time of the small section, and
A classification in which a target group for which the feature amount is obtained is clustered into a plurality of subsets based on the feature amount, using the runout value, the maximum pressure value of the small section, and the section time of the small section as feature amounts. The gait analyzer according to claim 5, further comprising a unit.

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