Static balance estimation device, static balance estimation system, static balance estimation method, and program

Abstract

This static balance estimation device comprises: a data acquisition unit that acquires feature value data in order to properly estimate static balance in daily life, the feature value data including a feature value used for estimating the static balance of a user, the feature value being extracted from the feature of the gait of the user; a storage unit that stores an estimation model for outputting a static balance index in response to the input of the feature value data; an estimation unit that inputs the acquired feature value data to the estimation model and estimates the static balance of the user according to the static balance index outputted from the estimation model; and an output unit that outputs information about the estimated static balance of the user.

Description

[Technical Field]

[0001] The present disclosure relates to a static balance estimation device and the like that estimates static balance using data related to gait. [Background technology]

[0002] With growing interest in healthcare, services that provide information based on the characteristics contained in walking patterns (also called gait) are attracting attention. For example, technology is being developed that analyzes gait based on sensor data measured by sensors mounted on footwear such as shoes. The time-series data of sensor data reveals the characteristics of gait events (also called walking events) related to physical conditions.

[0003] Patent Document 1 discloses an estimation device that estimates the type of footwear using sensor data acquired from a sensor attached to the footwear. The device in Patent Document 1 extracts gait features that are characteristic of walking while wearing the footwear using data acquired from the sensor attached to the footwear. The device in Patent Document 1 estimates the type of footwear based on the extracted gait features.

[0004] Patent Document 2 discloses a system for monitoring a user's mobility using an evaluation based on clinical mobility. The system of Patent Document 2 has an inertial measurement device including a gyroscope and an accelerometer. The system of Patent Document 2 uses the inertial measurement device to generate the user's inertial data indicating the user's mobility based on an evaluation according to clinical mobility. The system of Patent Document 2 locally logs the user's inertial data to a mobile device. The system of Patent Document 2 determines the position and orientation of the mobile device during an evaluation period based on clinical mobility by processing the locally logged user's inertial data in real time. The system of Patent Document 2 uses the position and orientation of the mobile device during an evaluation period based on clinical mobility to determine the user's body movement evaluation related to the evaluation based on clinical mobility. The system of Patent Document 2 displays at least a part of the body movement evaluation to the user. Patent Document 2 exemplifies several tests as evaluations based on clinical mobility. For example, the Timed Up and Go test, the Chair Stand test, the Four-Stage Balance test, gait analysis, the Single Leg Stand test, the Sit and Reach test, the Arm Curl test, postural stability, etc. are exemplified.

[0005] The Single Leg Stand test is one of the tests for evaluating static balance and stability. The result of the Single Leg Stand test is an important indicator for evaluating static balance and stability. In the Single Leg Stand test, the body moves to maintain stability from the pelvis to the lower limbs in order to control the sway of the center of gravity in the front-back, left-right directions. In the Single Leg Stand test, there is more movement control in the coronal plane and the horizontal plane than in the sagittal plane.

[0006] Non-Patent Document 1 reports the results of measuring the center of gravity sway of 33 healthy women while they stood on one leg with their eyes open and examining the relationship with major lower limb muscle strength and foot function. Non-Patent Document 1 reports that the tibialis anterior, abductor hallucis, flexor digitorum brevis, soleus, medial head of flexor hallucis brevis, quadriceps, and gluteus medius on the standing leg are related to maintaining posture while standing on one leg. Non-Patent Document 1 reports results suggesting that muscles related to foot grip strength, such as the tibialis anterior, abductor hallucis, flexor digitorum brevis, soleus, and medial head of flexor hallucis brevis, are particularly related to maintaining posture while standing on one leg.

[0007] Non-Patent Document 2 reports on the relationship between balance and muscles according to age. Non-Patent Document 2 reports that the older the person, the more hip joint muscles are related to balance than the knee joint or ankle joint. Non-Patent Document 2 reports that the difference between young and elderly people in the relationship between hip joint muscles and balance becomes significant, particularly in a one-leg standing test with the eyes closed.

[0008] Non-Patent Document 3 reports on the effects of aging and posture on maintaining one-leg standing. Non-Patent Document 3 reports that in elderly people, a significant increase in the velocity of the center of gravity in the anterior-posterior direction, the tilt angle of the pelvis, and the amount of muscle activity in the lower limbs was observed when the hip joint was kept flexed at 90 degrees compared to a posture in which the lower limbs were lightly raised so that they did not touch the ground. Non-Patent Document 3 also reports that a main effect was observed on the muscle activity of the tibialis anterior, rectus femoris, biceps femoris, gluteus medius, tensor fasciae latae, adductor muscles, and peroneus longus regarding the velocity of the center of gravity when standing on one leg. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] International Publication No. 2021 / 130907 [Patent Document 2] Special Publication No. 2021-524075 [Non-patent literature]

[0010] [Non-Patent Document 1] Shin Murata, “Relationship between center of gravity sway and foot function during one-legged standing with eyes open: A study of healthy women,” Physical Therapy Science, Vol. 19, pp. 245-249, 2004. [Non-patent document 2] D. Wiksten et.al., “The relationship between muscle and balance performance as a function of age”, Isokinetics and Exercise Science, Vol. 6 (2), pp.125-132, 1996. [Non-patent document 3] Mariko Nanbu, “The effects of aging and posture on maintaining one-legged standing,” Graduate Research Thesis, School of Health Sciences, Hokkaido University, 2013. Summary of the Invention [Problem to be solved by the invention]

[0011] The method of Patent Document 1 estimates the type of footwear using gait feature quantities of characteristic parts extracted from data acquired by sensors attached to the footwear. Patent Document 1 does not disclose estimating static balance using gait feature quantities of characteristic parts extracted from data acquired by sensors attached to the footwear.

[0012] Patent Document 2 exemplifies the use of inertial data measured by an inertial measurement unit to perform several tests in order to perform clinical mobility-based evaluation. In the method of Patent Document 2, it is necessary to actually perform several tests in order to perform clinical mobility-based evaluation.

[0013] Static balance can be evaluated if the results of a one-leg standing test can be evaluated, as in Non-Patent Documents 1 to 3. However, Non-Patent Documents 1 to 3 do not disclose a method for evaluating static balance in daily life, such as a one-leg standing test.

[0014] An object of the present disclosure is to provide a static balance estimation device or the like that can appropriately estimate static balance in daily life.

Means for Solving the Problems

[0015] A static balance estimation device according to an aspect of the present disclosure includes a data acquisition unit that acquires feature amount data including feature amounts used for estimating the static balance of a user, which are extracted from the characteristics of the user's gait; a storage unit that stores an estimation model that outputs a static balance index according to the input of the feature amount data; an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the static balance of the user according to the static balance index output from the estimation model; and an output unit that outputs information regarding the estimated static balance of the user.

[0016] In a static balance estimation method according to an aspect of the present disclosure, feature amount data including feature amounts used for estimating the static balance of a user, which are extracted from the characteristics of the user's gait, is acquired, the acquired feature amount data is input into an estimation model that outputs a static balance index according to the input of the feature amount data, the static balance of the user is estimated according to the static balance index output from the estimation model, and information regarding the estimated static balance of the user is output.

[0017] A program according to an aspect of the present disclosure causes a computer to execute a process of acquiring feature amount data including feature amounts used for estimating the static balance of a user, which are extracted from the characteristics of the user's gait; a process of inputting the acquired feature amount data into an estimation model that outputs a static balance index according to the input of the feature amount data; a process of estimating the static balance of the user according to the static balance index output from the estimation model; and a process of outputting information regarding the estimated static balance of the user.

Advantages of the Invention

[0018] According to the present disclosure, it becomes possible to provide a static balance estimation device or the like that can appropriately estimate static balance in daily life. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a block diagram showing an example of the configuration of a static balance estimation system according to a first embodiment. [Diagram 2] 1 is a block diagram showing an example of the configuration of a gait measurement device included in the static balance estimation system according to the first embodiment. FIG. [Figure 3] FIG. 1 is a conceptual diagram showing an example of the arrangement of a gait measurement device according to a first embodiment. [Figure 4] FIG. 2 is a conceptual diagram for explaining an example of the relationship between a local coordinate system and a world coordinate system set in the gait measurement device according to the first embodiment. [Figure 5] FIG. 1 is a conceptual diagram for explaining a human body surface used in explaining the gait measurement device according to the first embodiment. [Figure 6] FIG. 1 is a conceptual diagram for explaining a walking cycle used in explaining the gait measurement device according to the first embodiment. [Figure 7] FIG. 1 is a conceptual diagram for explaining gait parameters used in explaining the gait measurement device according to the first embodiment. [Figure 8] 3 is a graph for explaining an example of time-series data of sensor data measured by the gait measurement device according to the first embodiment. [Figure 9] FIG. 3 is a diagram for explaining an example of normalization of gait waveform data extracted from time-series data of sensor data measured by the gait measurement device according to the first embodiment. [Figure 10] FIG. 2 is a conceptual diagram for explaining an example of a walking phase cluster from which a feature amount data generation unit of the gait measurement device according to the first embodiment extracts feature amounts. [Figure 11] 1 is a block diagram showing an example of the configuration of a static balance estimation device included in a static balance estimation system according to a first embodiment. [Figure 12] FIG. 2 is a conceptual diagram for explaining a one-leg standing test that is an evaluation target of the static balance estimation system according to the first embodiment. [Figure 13] 1 is a table showing specific examples of feature amounts extracted for estimating one-leg standing time by a gait measurement device provided in the static balance estimation system according to the first embodiment. [Figure 14] 1 is a graph showing the correlation between a feature value F1 extracted by a gait measurement device included in the static balance estimation system according to the first embodiment and an actually measured one-leg standing time. [Figure 15] 10 is a graph showing the correlation between a feature amount F2 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and an actually measured one-leg standing time. [Figure 16] 10 is a graph showing the correlation between a feature value F3 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and an actually measured one-leg standing time. [Figure 17] 10 is a graph showing the correlation between a feature value F4 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and the actually measured one-leg standing time. [Figure 18] 10 is a graph showing the correlation between a feature value F5 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and the actually measured one-leg standing time. [Figure 19] 10 is a graph showing the correlation between a feature value F6 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and the actually measured one-leg standing time. [Figure 20] 10 is a graph showing the correlation between a feature value F7 extracted by the gait measurement device included in the static balance estimation system according to the first embodiment and the actually measured one-leg standing time. [Figure 21] 1 is a block diagram showing an example of estimation of one-leg standing time (static balance index) by a static balance estimation device included in the static balance estimation system according to the first embodiment. FIG. [Figure 22] 10 is a graph showing the correlation between the estimated value of one-leg standing time estimated using an estimation model generated by learning with gender, age, height, weight, and walking speed as explanatory variables, and the measured value of one-leg standing time. [Figure 23] 4 is a graph showing the correlation between the estimated value of one-leg standing time estimated by the static balance estimation device included in the static balance estimation system according to the first embodiment and the measured value of one-leg standing time. [Figure 24] 5 is a flowchart for explaining an example of the operation of the gait measurement device included in the static balance estimation system according to the first embodiment. [Figure 25] 5 is a flowchart illustrating an example of an operation of the static balance estimation device included in the static balance estimation system according to the first embodiment. [Figure 26] FIG. 2 is a conceptual diagram for explaining an application example of the static balance estimation system according to the first embodiment. [Figure 27] FIG. 10 is a block diagram showing an example of the configuration of a learning system according to a second embodiment. [Figure 28] FIG. 10 is a block diagram showing an example of the configuration of a learning device included in a learning system according to a second embodiment. [Figure 29] FIG. 10 is a conceptual diagram for explaining an example of learning by a learning device included in a learning system according to a second embodiment. [Figure 30] FIG. 10 is a block diagram showing an example of the configuration of a static balance estimation device according to a third embodiment. [Figure 31] FIG. 2 is a block diagram showing an example of a hardware configuration for executing control and processing in each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. However, the embodiments described below are limited in a manner that is technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In all drawings used to describe the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. Furthermore, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0021] (First Embodiment) First, a static balance estimation system according to the first embodiment will be described with reference to the drawings. The static balance estimation system of this embodiment measures sensor data related to the movement of the feet according to the user's walking. The static balance estimation system of this embodiment estimates the static balance of the user using the measured sensor data. Note that the sensor data is not limited to sensor data related to the movement of the feet, and may include features related to the gait. For example, the sensor data may be sensor data including features related to the gait measured using motion capture, smart apparel, or the like.

[0022] In this embodiment, an example of estimating the result of a one-leg stand test as the static balance is given. In particular, in this embodiment, an example of estimating the result of a one-leg stand test with eyes closed (closed-eye one-leg stand test) is given. In this embodiment, the result of the one-leg stand test is evaluated by the time (also called the one-leg standing time) of maintaining the state of lifting one leg 5 cm (centimeters) from the ground. The longer the one-leg standing time, the higher the result of the one-leg stand test. The method of this embodiment can also be applied to other than the closed-eye one-leg stand test. For example, the method of this embodiment can also be applied to a one-leg stand test with eyes open (open-eye one-leg stand test) and other variations of the one-leg stand test.

[0023] (Configuration) FIG. 1 is a block diagram showing an example of the configuration of a static balance estimation system 1 according to this embodiment. The static balance estimation system 1 includes a gait measurement device 10 and a static balance estimation device 13. In this embodiment, an example in which the gait measurement device 10 and the static balance estimation device 13 are configured as separate hardware will be described. For example, the gait measurement device 10 is installed in the footwear or the like of a subject (user) whose static balance is to be estimated. For example, the function of the static balance estimation device 13 is installed in a mobile terminal carried by the subject (user). Hereinafter, the configurations of the gait measurement device 10 and the static balance estimation device 13 will be described individually.

[0024] 〔Gait Measurement Device〕 2 is a block diagram showing an example of the configuration of gait measurement device 10. Gait measurement device 10 has sensor 11 and feature amount data generation unit 12. In this embodiment, an example is given in which sensor 11 and feature amount data generation unit 12 are integrated. Sensor 11 and feature amount data generation unit 12 may also be provided as separate devices.

[0025] 2, the sensor 11 has an acceleration sensor 111 and an angular velocity sensor 112. Fig. 2 shows an example in which the acceleration sensor 111 and the angular velocity sensor 112 are included in the sensor 11. The sensor 11 may include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Description of sensors other than the acceleration sensor 111 and the angular velocity sensor 112 that may be included in the sensor 11 will be omitted.

[0026] The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures acceleration (also called spatial acceleration) as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the feature data generation unit 12. For example, a piezoelectric, piezo-resistive, or capacitive sensor can be used as the acceleration sensor 111. There is no limitation on the measurement method of the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

[0027] The angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. The angular velocity sensor 112 measures angular velocity (also called spatial angular velocity) as a physical quantity related to foot movement. The angular velocity sensor 112 outputs the measured angular velocity to the feature data generation unit 12. For example, a vibration type or capacitance type sensor can be used as the angular velocity sensor 112. There is no limitation on the measurement method of the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0028] Sensor 11 is realized, for example, by an inertial measurement device that measures acceleration and angular velocity. As an example of an inertial measurement device, an IMU (Inertial Measurement Unit) can be mentioned. The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. Sensor 11 may be realized by an inertial measurement device such as a VG (Vertical Gyro) or an AHRS (Attitude Heading). Further, sensor 11 may be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). As long as sensor 11 can measure physical quantities related to the movement of the foot, it may be realized by a device other than an inertial measurement device.

[0029] FIG. 3 is a conceptual diagram showing an example in which the gait measurement device 10 is arranged inside the right-foot shoe 100. In the example of FIG. 3, the gait measurement device 10 is installed at a position corresponding to the back side of the arch of the foot. For example, the gait measurement device 10 is arranged on an insole inserted into the shoe 100. For example, the gait measurement device 10 may be arranged on the bottom surface of the shoe 100. For example, the gait measurement device 10 may be embedded in the main body of the shoe 100. The gait measurement device 10 may be detachable from the shoe 100 or may not be detachable from the shoe 100. As long as the gait measurement device 10 can measure sensor data related to the movement of the foot, it may be installed at a position other than the back side of the arch of the foot. Further, the gait measurement device 10 may be installed on socks worn by the user or on ornaments such as anklets worn by the user. Further, the gait measurement device 10 may be directly attached to the foot or embedded in the foot. FIG. 3 shows an example in which the gait measurement device 10 is installed in the right-foot shoe 100. The gait measurement device 10 may be installed in the shoes 100 for both feet.

[0030] In the example of FIG. 3 , a local coordinate system is set with the gait measurement device 10 (sensor 11) as the reference, and includes an x-axis in the left-right direction, a y-axis in the front-back direction, and a z-axis in the up-down direction. The x-axis is positive to the left, the y-axis is positive backward, and the z-axis is positive upward. The directions of the axes set in the sensor 11 may be the same for the left and right feet, or may be different for the left and right feet. For example, when sensors 11 manufactured to the same specifications are placed in left and right shoes 100, the up-down directions (directions of the Z-axis) of the sensors 11 placed in the left and right shoes 100 are the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right feet.

[0031] FIG. 4 is a conceptual diagram illustrating a local coordinate system (x-axis, y-axis, z-axis) set in gait measurement device 10 (sensor 11) installed on the backside of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set with respect to the ground. In the world coordinate system (x-axis, y-axis, z-axis), when a user is standing upright and facing the direction of travel, the user's sideways direction is set as the x-axis direction (positive for left), the direction of the user's back is set as the y-axis direction (positive for backward), and the direction of gravity is set as the z-axis direction (positive for vertical upward). Note that the example in FIG. 4 conceptually illustrates the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis), and does not accurately illustrate the relationship between the local coordinate system and the world coordinate system, which changes according to the user's walking.

[0032] FIG. 5 is a conceptual diagram illustrating planes (also called human body planes) set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. As shown in FIG. 5, when the user is standing upright with the center line of the feet facing the direction of travel, the world coordinate system and the local coordinate system coincide. In this embodiment, a rotation in the sagittal plane about the x-axis as the rotation axis is defined as roll, a rotation in the coronal plane about the y-axis as the rotation axis is defined as pitch, and a rotation in the horizontal plane about the z-axis as the rotation axis is defined as yaw. Furthermore, a rotation angle in the sagittal plane about the x-axis as the rotation axis is defined as roll angle, a rotation angle in the coronal plane about the y-axis as the rotation axis is defined as pitch angle, and a rotation angle in the horizontal plane about the z-axis as the rotation axis is defined as yaw angle.

[0033] As shown in FIG. 2, the feature amount data generation unit 12 (also referred to as a feature amount data generation device) includes an acquisition unit 121, a normalization unit 122, an extraction unit 123, a generation unit 125, and a feature amount data output unit 127. For example, the feature amount data generation unit 12 is realized by a microcomputer or a microcontroller that performs overall control and data processing of the gait measurement device 10. For example, the feature amount data generation unit 12 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, etc. The feature amount data generation unit 12 controls the acceleration sensor 111 and the angular velocity sensor 112 to measure the angular velocity and acceleration. For example, the feature amount data generation unit 12 may be mounted on the side of a portable terminal (not shown) carried by a subject (user).

[0034] The acquisition unit 121 acquires the acceleration in the three-axis directions from the acceleration sensor 111. Also, the acquisition unit 121 acquires the angular velocity around the three axes from the angular velocity sensor 112. For example, the acquisition unit 121 performs AD conversion (Analog-to-Digital Conversion) on physical quantities (analog data) such as the acquired angular velocity and acceleration. Note that the physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. The acquisition unit 121 outputs the converted digital data (also referred to as sensor data) to the normalization unit 122. The acquisition unit 121 may be configured to store the sensor data in a storage unit (not shown). The sensor data at least includes acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes an acceleration vector in the three-axis directions. The angular velocity data includes an angular velocity vector around the three axes. The acquisition time of those data is associated with the acceleration data and the angular velocity data. Also, the acquisition unit 121 may apply corrections such as mounting error, temperature correction, and linearity correction to the acceleration data and the angular velocity data.

[0035] The normalization unit 122 acquires sensor data from the acquisition unit 121. The normalization unit 122 extracts time series data for one walking cycle (also referred to as walking waveform data) from the time series data of accelerations in three axial directions and angular velocities around three axes included in the sensor data. The normalization unit 122 normalizes the time of the extracted walking waveform data for one walking cycle to a walking cycle of 0 to 100% (percent) (also referred to as first normalization). Timings such as 1% and 10% included in the 0 to 100% walking cycle are also referred to as walking phases. The normalization unit 122 also normalizes the first normalized walking waveform data for one walking cycle so that the stance phase is 60% and the swing phase is 40% (also referred to as second normalization). The stance phase is a period when at least a part of the sole of the foot is in contact with the ground. The swing phase is a period when the sole of the foot is off the ground. By subjecting the walking waveform data to second normalization, it is possible to suppress fluctuations in the walking phase from which feature values are extracted due to the influence of disturbances.

[0036] FIG. 6 is a conceptual diagram illustrating a step cycle based on the right foot. The step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 6 represents one step cycle of the right foot, starting from the point when the heel of the right foot hits the ground and ending from the point when the heel of the right foot hits the ground again. The horizontal axis of FIG. 6 is first normalized so that the step cycle is 100%. The horizontal axis of FIG. 6 is also second normalized so that the stance phase is 60% and the swing phase is 40%. One step cycle of one leg is roughly divided into a stance phase in which at least a portion of the sole of the foot is in contact with the ground, and a swing phase in which the sole of the foot is off the ground. The stance phase is further divided into a load response period T1, a mid-stance period T2, a final stance period T3, and an early swing period T4. The swing phase is further divided into an early swing period T5, a mid-swing period T6, and a final swing period T7. Note that FIG. 6 is an example and does not limit the periods that make up a walking cycle or the names of these periods.

[0037] As shown in FIG. 6, in walking, a plurality of events (also referred to as walking events) occur. E1 represents the event where the heel of the right foot touches the ground (heel contact) (HC: Heel Contact). E2 represents the event where the tip of the left foot leaves the ground while the sole of the right foot is in contact with the ground (opposite toe off) (OTO: Opposite Toe Off). E3 represents the event where the heel of the right foot is lifted while the sole of the right foot is in contact with the ground (heel rise) (HR: Heel Rise). E4 is the event where the heel of the left foot touches the ground (opposite heel strike) (OHS: Opposite Heel Strike). E5 represents the event where the tip of the right foot leaves the ground while the sole of the left foot is in contact with the ground (toe off) (TO: Toe Off). E6 represents the event where the left and right feet cross while the sole of the left foot is in contact with the ground (foot adjacent) (FA: Foot Adjacent). E7 represents the event where the tibia of the right foot becomes substantially perpendicular to the ground while the sole of the left foot is in contact with the ground (tibia vertical) (TV: Tibia Vertical). E8 represents the event where the heel of the right foot touches the ground (heel contact) (HC: Heel Contact). E8 corresponds to the end point of the walking cycle starting from E1 and also corresponds to the starting point of the next walking cycle. Note that FIG. 6 is an example and does not limit the events occurring in walking or the names of those events.

[0038] FIG. 7 is a conceptual diagram illustrating an example of gait parameters. FIG. 7 illustrates the right foot step length SR, left foot step length SL, stride length T, step width W, foot angle F, and rotational distance D. FIG. 7 also illustrates a progression axis P that is parallel to the axis of progression (Y axis) and corresponds to a trajectory connecting the midpoints of the left and right feet. The right foot step length SR is the difference in the Y coordinates of the right heel and the left heel when the state transitions from a state where the sole of the left foot is on the ground to a state where the heel of the right foot, which is swung in the direction of progression, lands on the ground. The left foot step length SL is the difference in the Y coordinates of the left heel and the right heel when the state transitions from a state where the sole of the right foot is on the ground to a state where the heel of the left foot, which is swung in the direction of progression, lands on the ground. The stride length T is the sum of the right foot step length SR and the left foot step length SL. The step width W is the distance between the right and left feet. In FIG. 7, step width W is the difference between the X coordinate of the center line of the heel of the right foot when it is in contact with the ground and the X coordinate of the center line of the heel of the left foot when it is in contact with the ground. Foot angle F is the angle between the center line of the foot and the direction of travel (Y axis) when the sole of the foot is in contact with the ground. In this embodiment, the foot angle is evaluated when the foot is in contact with the ground during the stance phase. Rotational distance D is the distance between the axis of travel P and the foot at the point when the center axis of the foot is farthest from the axis of travel P during the swing phase. In this embodiment, rotational distance D is normalized by height because it is affected by the length of the lower limbs.

[0039] FIG. 8 is a diagram illustrating an example of detecting heel strike HC and toe lift TO from time series data (solid line) of forward acceleration (Y-direction acceleration). The timing of heel strike HC is the timing of the minimum peak immediately after the maximum peak that appears in the time series data of forward acceleration (Y-direction acceleration). The maximum peak that marks the timing of heel strike HC corresponds to the maximum peak of the gait waveform data for one step cycle. The section between consecutive heel strikes HC is one step cycle. The timing of toe lift TO is the timing of the rise of the maximum peak that appears after the stance phase period during which no fluctuations appear in the time series data of forward acceleration (Y-direction acceleration). FIG. 8 also shows time series data (dashed line) of roll angle (angular velocity about the X-axis). The midpoint between the timing of the minimum roll angle and the timing of the maximum roll angle corresponds to the mid-stance phase. For example, parameters such as walking speed, stride length, circumduction, internal rotation / external rotation, and plantar flexion / dorsiflexion (also called gait parameters) can be determined using the mid-stance phase as a reference.

[0040] FIG. 9 is a diagram illustrating an example of gait waveform data normalized by the normalization unit 122. The normalization unit 122 detects heel strikes HC and toe lifts TO from time-series data of forward acceleration (Y-direction acceleration). The normalization unit 122 extracts the interval between successive heel strikes HC as gait waveform data for one step cycle. The normalization unit 122 converts the horizontal axis (time axis) of the gait waveform data for one step cycle into a gait cycle of 0 to 100% by first normalization. In FIG. 9, the gait waveform data after the first normalization is shown by a dashed line. In the gait waveform data (dashed line) after the first normalization, the timing of toe lifts TO is shifted from 60%.

[0041] In the example of FIG. 9, the normalization unit 122 normalizes the section from the heel contact HC with a walking phase of 0% to the toe-off TO following the heel contact HC to 0 to 60%. Further, the normalization unit 122 normalizes the section from the toe-off TO to the heel contact HC with a subsequent walking phase of 100% following the toe-off TO to 60 to 100%. As a result, the walking waveform data for one walking cycle is normalized into a section with a walking cycle of 0 to 60% (stance phase) and a section with a walking cycle of 60 to 100% (swing phase). In FIG. 9, the walking waveform data after the second normalization is shown by a solid line. In the walking waveform data (solid line) after the second normalization, the timing of the toe-off TO coincides with 60%.

[0042] FIGS. 8 to 9 show an example of extracting / normalizing the walking waveform data for one walking cycle based on the acceleration in the traveling direction (Y-direction acceleration). Regarding the acceleration / angular velocity other than the acceleration in the traveling direction (Y-direction acceleration), the normalization unit 122 extracts / normalizes the walking waveform data for one walking cycle in accordance with the walking cycle of the acceleration in the traveling direction (Y-direction acceleration). Further, the normalization unit 122 may generate time-series data of angles around three axes by integrating the time-series data of the angular velocities around the three axes. In that case, the normalization unit 122 also extracts / normalizes the walking waveform data for one walking cycle in accordance with the walking cycle of the acceleration in the traveling direction (Y-direction acceleration) regarding the angles around the three axes.

[0043] The normalization unit 122 may extract / normalize the gait waveform data for one step gait cycle based on acceleration / angular velocity other than the forward acceleration (Y-direction acceleration) (not shown in the drawings). For example, the normalization unit 122 may detect heel strike HC and toe lift TO from time series data of vertical acceleration (Z-direction acceleration). The timing of heel strike HC is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of vertical acceleration (Z-direction acceleration) becomes almost zero. The minimum peak that marks the timing of heel strike HC corresponds to the minimum peak of the gait waveform data for one step gait cycle. The section between consecutive heel strikes HC is one step gait cycle. The timing of toe-off TO is the timing of an inflection point in the time-series data of vertical acceleration (Z-direction acceleration) where the vertical acceleration (Z-direction acceleration) gradually increases after passing through a section of small fluctuations following the maximum peak immediately after heel-contact HC. The normalization unit 122 may also extract / normalize the gait waveform data for one walking cycle based on both the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration). The normalization unit 122 may also extract / normalize the gait waveform data for one walking cycle based on acceleration, angular velocity, angle, etc. other than the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration).

[0044] The extraction unit 123 acquires walking waveform data for one step gait cycle normalized by the normalization unit 122. The extraction unit 123 extracts feature quantities used for estimating static balance from the walking waveform data for one step gait cycle. The extraction unit 123 extracts feature quantities for each walking phase cluster from a walking phase cluster that integrates temporally consecutive walking phases based on preset conditions. A walking phase cluster includes at least one walking phase. A walking phase cluster may also include a single walking phase. The walking waveform data and walking phases from which feature quantities used for estimating static balance are extracted will be described later.

[0045] FIG. 10 is a conceptual diagram for explaining the extraction of feature quantities for estimating static balance from gait waveform data for one gait cycle. For example, the extraction unit 123 extracts temporally consecutive gait phases i to i + m as a gait phase cluster C (i and m are natural numbers). The gait phase cluster C includes m gait phases (constituent elements). That is, the number of gait phases (constituent elements) constituting the gait phase cluster C (also referred to as the number of constituent elements) is m. FIG. 10 shows an example where the gait phase is an integer value, but the gait phase may be subdivided down to the decimal point. When the gait phase is subdivided down to the decimal point, the number of constituent elements of the gait phase cluster C becomes a number corresponding to the number of data points in the interval of the gait phase cluster. The extraction unit 123 extracts feature quantities from each of the gait phases i to i + m. When the gait phase cluster C is composed of a single gait phase j, the extraction unit 123 extracts a feature quantity from that single gait phase j (j is a natural number).

[0046] The generation unit 125 applies a feature quantity composition formula to the feature quantities (first feature quantities) extracted from each of the gait phases constituting the gait phase cluster to generate a feature quantity (second feature quantity) of the gait phase cluster. The feature quantity composition formula is a calculation formula set in advance for generating the feature quantity of the gait phase cluster. For example, the feature quantity composition formula is a calculation formula related to arithmetic operations. For example, the second feature quantity calculated using the feature quantity composition formula is the integrated average value, arithmetic average value, slope, variation, etc. of the first feature quantities in each gait phase included in the gait phase cluster. For example, the generation unit 125 applies a calculation formula for calculating the slope and variation of the first feature quantities extracted from each of the gait phases constituting the gait phase cluster as the feature quantity composition formula. For example, when the gait phase cluster is composed of a single gait phase, since the slope and variation cannot be calculated, a feature quantity composition formula for calculating the integrated average value, arithmetic average value, etc. may be used.

[0047] The feature data output unit 127 outputs the feature data for each walking phase cluster generated by the generation unit 125. The feature data output unit 127 outputs the feature data of the generated walking phase cluster to the static balance estimation device 13, which uses the feature data.

[0048] [Static balance estimation device] 11 is a block diagram showing an example of the configuration of the static balance estimation device 13. The static balance estimation device 13 includes a data acquisition unit 131, a storage unit 132, an estimation unit 133, and an output unit 135.

[0049] Data acquisition unit 131 acquires feature amount data from gait measurement device 10. Data acquisition unit 131 outputs the received feature amount data to estimation unit 133. Data acquisition unit 131 may receive the feature amount data from gait measurement device 10 via a wired connection such as a cable, or may receive the feature amount data from gait measurement device 10 via wireless communication. For example, data acquisition unit 131 is configured to receive the feature amount data from gait measurement device 10 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of data acquisition unit 131 may be compliant with standards other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0050] The storage unit 132 stores an estimation model that estimates the one-leg standing time as static balance using feature amount data extracted from the gait waveform data. The storage unit 132 stores an estimation model that has learned the relationship between the feature amount data related to the one-leg standing time of multiple subjects and the one-leg standing time. For example, the storage unit 132 stores an estimation model that estimates the one-leg standing time that has been learned for multiple subjects. The one-leg standing time is affected by age and height. Therefore, the storage unit 132 may store an estimation model according to attribute data related to at least one of age and height.

[0051] FIG. 12 is a conceptual diagram for explaining the single-leg standing test. FIG. 12 shows a state where the subject closes his / her eyes and raises one leg 5 cm (centimeters) from the ground. In the present embodiment, the closed-eye single-leg standing test is taken as an example. The method of the present embodiment can also be applied to single-leg standing tests other than the closed-eye single-leg standing test, such as the open-eye single-leg standing test performed with the eyes open.

[0052] Static balance can be evaluated according to the time that the closed-eye single-leg standing position can be maintained (also referred to as the closed-eye single-leg standing time). When the closed-eye single-leg standing time is 30 seconds or more, the static balance is high and the risk of falling is low. When the closed-eye single-leg standing time is within the range of 15 to 30 seconds, the static balance is low and there is a risk of falling. When the closed-eye single-leg standing time is less than 15 seconds, the static balance is quite low and the risk of falling is very high. The evaluation criteria for static balance according to the closed-eye single-leg standing time mentioned here are for reference, and can be set according to the situation. For example, the evaluation criteria for static balance according to the closed-eye single-leg standing time also vary depending on the subject's medical history. Also, in the case of single-leg standing tests other than the closed-eye single-leg standing test, the evaluation criteria may be set according to those tests. Hereinafter, the time that the single-leg standing position can be maintained, including the closed-eye single-leg standing time, is referred to as the single-leg standing time.

[0053] The estimation model may be stored in the storage unit 132 at the time of factory shipment of the product or at the time of calibration before the user uses the static balance estimation system 1. For example, it may be configured to use an estimation model stored in a storage device such as an external server. In that case, it may be configured to use the estimation model via an interface (not shown) connected to the storage device.

[0054] The estimation unit 133 acquires feature amount data from the data acquisition unit 131. The estimation unit 133 uses the acquired feature amount data to estimate the one-leg standing time as static balance. The estimation unit 133 inputs the feature amount data to an estimation model stored in the storage unit 132. The estimation unit 133 outputs an estimation result corresponding to the static balance (one-leg standing time) output from the estimation model. When using an estimation model stored in an external storage device constructed in the cloud, a server, or the like, the estimation unit 133 is configured to use the estimation model via an interface (not shown) connected to the storage device.

[0055] The output unit 135 outputs the static balance estimation result obtained by the estimation unit 133. For example, the output unit 135 displays the static balance estimation result on the screen of a mobile terminal of the subject (user). For example, the output unit 135 outputs the estimation result to an external system or the like that uses the estimation result. There are no particular limitations on how the static balance output from the static balance estimation device 13 can be used.

[0056] For example, the static balance estimation device 13 is connected to an external system or the like constructed in a cloud or a server via a mobile terminal (not shown) carried by a subject (user). The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a portable communication device having a communication function such as a smartphone, a smartwatch, or a mobile phone. For example, the static balance estimation device 13 is connected to the mobile terminal via a wired connection such as a cable. For example, the static balance estimation device 13 is connected to the mobile terminal via wireless communication. For example, the static balance estimation device 13 is connected to the mobile terminal via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the static balance estimation device 13 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark). The estimation result of the static balance may be used by an application installed in the mobile terminal. In that case, the mobile terminal executes processing using the estimation result by application software or the like installed in the mobile terminal.

[0057] 〔Estimation of single-leg standing time〕 Next, the correlation between the single-leg standing time and the feature amount data will be described with verification examples. FIG. 13 is a correspondence table summarizing the feature amounts used for the estimation of the single-leg standing time. The correspondence table in FIG. 13 associates the feature amount number, the walking waveform data from which the feature amount is extracted, the walking phase (%) from which the walking phase cluster is extracted, and the related muscles. The single-leg standing time is correlated with the gluteus medius muscle, the long adductor muscle, the sartorius muscle, and the internal and external rotator muscle groups. Therefore, for the estimation of the single-leg standing time, the feature amounts F1 to F7 extracted from the walking phases in which these features appear are used.

[0058] Figures 14 to 20 show the verification results of the correlation between the single-leg standing time and the feature data. Figures 14 to 20 show the results of verification for a total of 62 subjects, including 27 men and 35 women aged 60 to 85 years. Figures 14 to 20 show the results of verifying the correlation between the estimated values estimated using the feature amounts extracted according to the walking while wearing the footwear equipped with the gait measurement device 10 and the measured values (true values) of the single-leg standing time.

[0059] The feature amount F1 is extracted from the section of the gait waveform data Ax regarding the time-series data of the lateral acceleration (X-direction acceleration) in the gait phase of 13 - 19%. The gait phase of 13 - 19% is included in the mid-stance phase T2. The feature amount F1 mainly includes features related to the movement of the gluteus medius muscle. Figure 14 shows the verification results of the correlation between the feature amount F1 and the single-leg standing time. The horizontal axis of the graph in Figure 14 is the normalized acceleration. The correlation coefficient R between the feature amount F1 and the single-leg standing time was -0.434.

[0060] The feature amount F2 is extracted from the section of the gait waveform data Az regarding the time-series data of the vertical acceleration (Z-direction acceleration) in the gait phase of 95%. The gait phase of 95% is the end stage of the swing leg end phase T7. The feature amount F2 mainly includes features related to the movement of the gluteus medius muscle. Figure 15 shows the verification results of the correlation between the feature amount F2 and the single-leg standing time. The horizontal axis of the graph in Figure 15 is the normalized acceleration. The correlation coefficient R between the feature amount F2 and the single-leg standing time was -0.295.

[0061] The feature amount F3 is extracted from the section of the gait waveform data Gy regarding the time-series data of the angular velocity in the coronal plane (around the Y-axis) in the gait phase of 64 - 65%. The gait phase of 64 - 65% is included in the initial swing phase T5. The feature amount F3 mainly includes features related to the movement of the tensor fasciae latae muscle and the sartorius muscle. Figure 16 shows the verification results of the correlation between the feature amount F3 and the single-leg standing time. The horizontal axis of the graph in Figure 16 is the normalized angular velocity. The correlation coefficient R between the feature amount F3 and the single-leg standing time was -0.303.

[0062] Feature quantity F4 is extracted from the section of the walking waveform data Gz related to the time-series data of the angular velocity in the horizontal plane (around the Z axis) in the range of walking phases 11 - 16%. The walking phases 11 - 16% are included in the mid-stance phase T2. Feature quantity F4 mainly includes features related to the movement of the gluteus medius muscle. Figure 17 shows the verification result of the correlation between feature quantity F4 and the single-leg stance time. The horizontal axis of the graph in Figure 17 is the normalized angular velocity. The correlation coefficient R between feature quantity F4 and the single-leg stance time was -0.462.

[0063] Feature quantity F5 is extracted from the section of the walking waveform data Gz related to the time-series data of the angular velocity in the horizontal plane (around the Z axis) in the range of walking phases 57 - 58%. The walking phases 57 - 58% are included in the pre-swing phase T4. Feature quantity F5 mainly includes features related to the movement of the tensor fasciae latae muscle and the sartorius muscle. Figure 18 shows the verification result of the correlation between feature quantity F5 and the single-leg stance time. The horizontal axis of the graph in Figure 18 is the normalized angular velocity. The correlation coefficient R between feature quantity F4 and the single-leg stance time was 0.393.

[0064] Feature quantity F6 is extracted from the section of the walking waveform data Ez related to the time-series data of the angle (posture angle) in the horizontal plane (around the Z axis) at the walking phase of 100%. The walking phase of 100% corresponds to the timing of heel strike when switching from the end-swing phase T7 to the load response phase T1. The feature quantity of the walking waveform data Ez at the walking phase of 100% corresponds to the foot angle when the sole of the foot is in contact with the ground. Feature quantity F6 mainly includes features related to the movement of the gluteus medius muscle. Figure 19 shows the verification result of the correlation between feature quantity F6 and the single-leg stance time. The horizontal axis of the graph in Figure 19 is the angle in the horizontal plane (plantar angle). The correlation coefficient R between feature quantity F6 and the single-leg stance time was -0.310. Feature quantity F6 is not an essential feature quantity for estimating the single-leg stance time, but it improves the estimation accuracy of the single-leg stance time.

[0065] Feature quantity F7 is the distance (amount of circumduction) between the progression axis and the foot at the timing when the central axis of the foot is farthest from the progression axis during the swing phase. Feature quantity F7 is the amount of circumduction normalized by the height of the subject. Feature quantity F7 mainly includes features related to the movement of the abductor and adductor muscle groups. FIG. 20 shows the verification result of the correlation between feature quantity F7 and the single-leg stance time. The horizontal axis of the graph in FIG. 20 is the amount of circumduction normalized by height (normalized amount of circumduction). The correlation coefficient R between feature quantity F7 and the single-leg stance time was 0.200.

[0066] FIG. 21 is a conceptual diagram showing an example in which an estimated value of the single-leg stance time is output by inputting feature quantities F1 to F7 extracted from sensor data measured as the user walks into an estimation model 151 constructed in advance to estimate the single-leg stance time as a static balance. The estimation model 151 outputs the single-leg stance time, which is an index of static balance, in response to the input of feature quantities F1 to F7. For example, the estimation model 151 is generated by learning using teacher data with feature quantities F1 to F7 used for estimating the single-leg stance time as explanatory variables and the single-leg stance time as the objective variable. There is no limitation on the estimation result of the estimation model 151 as long as an estimation result regarding the single-leg stance time, which is an index of static balance, is output in response to the input of feature quantity data for estimating the single-leg stance time. For example, the estimation model 151 may be a model that estimates the single-leg stance time using attribute data (age, height) as explanatory variables in addition to feature quantities F1 to F7 used for estimating the single-leg stance time.

[0067] For example, in the storage unit 132, an estimation model for estimating the single-leg stance time is stored using the multiple regression prediction method. For example, in the storage unit 132, parameters for estimating the single-leg stance time are stored using the following formula (1). Single-leg stance time = a1×F1 + a2×F2 + a3×F3 + a4×F4 + a5×F5 + a6×F6 + a7×F7 + a0 ··· (1) In the above formula (1), F1, F2, F3, F4, F5, F6, and F7 are feature quantities for each walking phase cluster used for estimating the single-leg standing time shown in the correspondence table of FIG. 13. a1, a2, a3, a4, a5, a6, and a7 are coefficients multiplied by F1, F2, F3, F4, F5, F6, and F7. a0 is a constant term. For example, the storage unit 132 stores a0, a1, a2, a3, a4, a5, a6, and a7.

[0068] Next, the results of evaluating the estimation model 151 generated using the measurement data of the 62 subjects described above are shown. Here, a verification example (FIG. 22) in which static balance (single-leg standing time) is estimated using the attributes of the subject (including walking speed) and a verification example (FIG. 23) in which static balance (single-leg standing time) is estimated using the feature quantities of the subject's gait are compared. FIGS. 22 and 23 show the results of testing the estimation model generated using the measurement data of 61 subjects by the LOSO (Leave-One-Subject-Out) method using the measurement data of the remaining 1 subject. FIGS. 22 and 23 show the results of performing LOSO on all 62 subjects and corresponding the predicted values and the measured values (true values) by the test. The test results of LOSO were evaluated by the values of the intraclass correlation coefficient ICC (Intraclass Correlation Coefficients), the mean absolute error MAE (Mean Absolute Error), and the coefficient of determination R2. For the intraclass correlation coefficient ICC, the intraclass correlation coefficient ICC(2,1) was used to evaluate the inter-rater reliability.

[0069] FIG. 22 shows the verification results of the estimation model of the comparative example in which the teacher data with gender, age, height, weight, and walking speed as explanatory variables and the single-leg standing time as the objective variable was learned. In the estimation model of the comparative example, the intraclass correlation coefficient ICC(2,1) was 0.11, the mean absolute error MAE was 3.97, and the coefficient of determination R2 was 0.02.

[0070] FIG. 23 shows the verification results of the estimation model 151 of this embodiment, which was trained with training data using feature quantities F1 to F7, age, and height as explanatory variables and single-leg standing time as the objective variable. The estimation model 151 of this embodiment had an intraclass correlation coefficient ICC(2, 1) of 0.571, a mean absolute error MAE of 3.63, and a coefficient of determination R2 of 0.35. That is, compared to the estimation model of the comparative example, the estimation model 151 of this embodiment is more reliable, has smaller errors, and the objective variable is adequately explained by the explanatory variables. That is, according to the method of this embodiment, it is possible to generate an estimation model 151 that is more reliable, has smaller errors, and adequately explains the objective variable by the explanatory variables, compared to an estimation model that uses only attributes and walking speed.

[0071] (operation) Next, the operation of static balance estimation system 1 will be described with reference to the drawings. Here, we will explain separately gait measurement device 10 and static balance estimation device 13 included in static balance estimation system 1. Regarding gait measurement device 10, we will explain the operation of feature amount data generation unit 12 included in gait measurement device 10.

[0072] [Gait measurement device] Fig. 24 is a flowchart for explaining the operation of the feature amount data generation unit 12 included in the gait measurement device 10. In the explanation following the flowchart of Fig. 24, the feature amount data generation unit 12 will be described as the subject of the operation.

[0073] In FIG. 24, first, the feature amount data generator 12 acquires time-series data of sensor data relating to gait (step S101).

[0074] Next, the feature amount data generating unit 12 extracts gait waveform data for one gait cycle from the time series data of the sensor data (step S102). The feature amount data generating unit 12 detects heel strikes and toe lifts from the time series data of the sensor data. The feature amount data generating unit 12 extracts time series data of the sections between successive heel strikes as gait waveform data for one gait cycle.

[0075] Next, the feature amount data generating unit 12 normalizes the extracted walking waveform data for one step cycle (step S103). The feature amount data generating unit 12 normalizes the walking waveform data for one step cycle to a walking cycle of 0 to 100% (first normalization). Furthermore, the feature amount data generating unit 12 normalizes the ratio of the stance phase to the swing phase of the first normalized walking waveform data for one step cycle to 60:40 (second normalization).

[0076] Next, the feature data generator 12 extracts feature values from the normalized walking waveform, from the walking phases used for estimating static balance (step S104). For example, the feature data generator 12 extracts feature values to be input to a pre-constructed estimation model.

[0077] Next, the feature data generator 12 uses the extracted feature to generate a feature for each walking phase cluster (step S105).

[0078] Next, the feature amount data generator 12 integrates the feature amounts for each walking phase cluster to generate feature amount data for one walking cycle (step S106).

[0079] Next, the feature amount data generator 12 outputs the generated feature amount data to the static balance estimation device 13 (step S107).

[0080] [Static balance estimation device] Fig. 25 is a flowchart for explaining the operation of the static balance estimation device 13. In the explanation following the flowchart of Fig. 25, the static balance estimation device 13 will be described as the subject of the operation.

[0081] In FIG. 25, first, the static balance estimation device 13 acquires feature amount data generated using sensor data related to gait (step S131).

[0082] Next, the static balance estimation device 13 inputs the acquired feature amount data into an estimation model for estimating the static balance (single-leg standing time) (step S132).

[0083] Next, the static balance estimation device 13 estimates the static balance of the user according to the output (estimated value) from the estimation model (step S133). For example, the static balance estimation device 13 estimates the single-leg standing time of the user as the static balance.

[0084] Next, the static balance estimation device 13 outputs information regarding the estimated static balance (step S134). For example, the static balance is output to a terminal device (not shown) carried by the user. For example, the static balance is output to a system that executes processing using the static balance.

[0085] (Application Example) Next, an application example according to the present embodiment will be described with reference to the drawings. In the following application example, an example is shown in which the function of the static balance estimation device 13 installed in a portable terminal carried by a user estimates information regarding the static balance using the feature amount data measured by the gait measurement device 10 disposed on the shoe.

[0086] FIG. 26 is a conceptual diagram showing an example in which the estimation result by the static balance estimation device 13 is displayed on the screen of the portable terminal 160 carried by a user walking while wearing the shoe 100 on which the gait measurement device 10 is disposed. FIG. 26 is an example in which information according to the estimation result of the static balance using the feature amount data corresponding to the sensor data measured during the user's walking is displayed on the screen of the portable terminal 160.

[0087] FIG. 26 shows an example in which information corresponding to the estimated value of the one-leg standing time, which is a static balance, is displayed on the screen of the mobile terminal 160. In the example of FIG. 26, as an estimated result of the static balance, the estimated value of the one-leg standing time is displayed on the display unit of the mobile terminal 160. Further, in the example of FIG. 26, information regarding the estimated result of the static balance, "The static balance is deteriorating.", is displayed on the display unit of the mobile terminal 160 according to the estimated value of the one-leg standing time, which is a static balance. Further, in the example of FIG. 26, recommendation information corresponding to the estimated result of the static balance, "Training A is recommended. Please watch the following video.", is displayed on the display unit of the mobile terminal 160 according to the estimated value of the one-leg standing time, which is a static balance. The user who has confirmed the information displayed on the display unit of the mobile terminal 160 can practice training that leads to an increase in static balance by referring to the video of Training A and exercising according to the recommendation information.

[0088] As described above, the static balance estimation system of the present embodiment includes a gait measurement device and a static balance estimation device. The gait measurement device includes a sensor and a feature amount data generation unit. The sensor has an acceleration sensor and an angular velocity sensor. The sensor measures the spatial acceleration using the acceleration sensor. The sensor measures the spatial angular velocity using the angular velocity sensor. The sensor generates sensor data regarding the movement of the foot using the measured spatial acceleration and spatial angular velocity. The sensor outputs the generated sensor data to the feature amount data generation unit. The feature amount data generation unit acquires time-series data of the sensor data regarding the movement of the foot. The feature amount data generation unit extracts gait waveform data for one gait cycle from the time-series data of the sensor data. The feature amount data generation unit normalizes the extracted gait waveform data. The feature amount data generation unit extracts features used for estimating the static balance from a gait phase cluster composed of at least one gait phase that is temporally continuous from the normalized gait waveform data. The feature amount data generation unit generates feature amount data including the extracted features. The feature amount data generation unit outputs the generated feature amount data.

[0089] The static balance estimation device includes a data acquisition unit, a storage unit, an estimation unit, and an output unit. The data acquisition unit acquires feature data including feature amounts used to estimate the user's static balance, extracted from the characteristics of the user's gait. The storage unit stores an estimation model that outputs a static balance index according to input of the feature data. The estimation unit inputs the acquired feature data to the estimation model. The estimation unit estimates the user's static balance according to the static balance index output from the estimation model. The output unit outputs information related to the estimated static balance.

[0090] The static balance estimation system of this embodiment estimates the static balance of a user using feature quantities extracted from the characteristics of the user's gait. Therefore, the static balance estimation system of this embodiment can appropriately estimate static balance in daily life without using any equipment for measuring static balance.

[0091] In one aspect of this embodiment, the data acquisition unit acquires feature data including feature amounts extracted from gait waveform data generated using time-series data of sensor data related to foot movement. The data acquisition unit acquires feature data including feature amounts used to estimate a performance value (single-leg standing time) of a single-leg standing test as a static balance index. According to this aspect, by using the sensor data related to foot movement, static balance can be appropriately estimated in daily life without using any equipment for measuring static balance.

[0092] In one aspect of this embodiment, the storage unit stores an estimation model generated by learning using training data related to multiple subjects. The estimation model is generated by learning using training data in which feature values used to estimate static balance indices are explanatory variables and the static balance indices of the multiple subjects are objective variables. The estimation unit inputs feature value data acquired about the user into the estimation model. The estimation unit estimates the user's static balance according to the user's static balance indices output from the estimation model. According to this aspect, static balance can be appropriately estimated in daily life without using any equipment for measuring static balance.

[0093] In one aspect of this embodiment, the storage unit stores an estimation model trained using explanatory variables including the subject's attribute data (age, height). The estimation unit inputs feature data and attribute data (age, height) related to the user into the estimation model. The estimation unit estimates the user's static balance according to the user's static balance index output from the estimation model. In this aspect, static balance is estimated including attribute data (age, height) that affect static balance. Therefore, according to this aspect, static balance can be measured with higher accuracy.

[0094] In one aspect of this embodiment, the storage unit stores an estimation model generated by learning using training data for multiple subjects. The estimation model is generated by learning using training data in which feature quantities extracted from gait waveform data of the multiple subjects are used as explanatory variables and static balance indices of the multiple subjects are used as objective variables. For example, feature quantities related to the activity of the gluteus medius muscle extracted from the end of the final swing phase and the mid-stance phase are included in the explanatory variables. For example, feature quantities related to the activity of the adductor longus and sartorius muscles extracted from the early swing phase and the early swing phase are included in the explanatory variables. For example, feature quantities related to the activity of the adductor-externus muscle group during the swing phase are included in the explanatory variables. The estimation unit inputs feature quantity data acquired during the user's gait into the estimation model. The estimation unit estimates the user's static balance based on the user's static balance indices output from the estimation model. According to this aspect, static balance that is more suited to physical activity can be estimated using the estimation model that has learned feature quantities related to the activity of muscles that affect static balance.

[0095] In one aspect of this embodiment, the storage unit stores estimation models generated by learning using training data for multiple subjects, in which multiple feature values extracted from gait waveform data are used as explanatory variables and static balance related to the subject's static balance index is used as a response variable. For example, feature values extracted from the mid-stance phase of gait waveform data of lateral acceleration are included in the explanatory variables. For example, feature values extracted from the end of the final swing phase of gait waveform data of vertical acceleration are included in the explanatory variables. For example, feature values extracted from the early swing phase of gait waveform data of angular velocity in the coronal plane are included in the explanatory variables. For example, feature values extracted from the mid-stance phase and early swing phase of gait waveform data of angular velocity in the horizontal plane are included in the explanatory variables. For example, feature values extracted from the timing of heel strike, which marks the transition from the final swing phase to the load response phase, of gait waveform data of angle in the horizontal plane are included in the explanatory variables. For example, feature values related to the amount of circumduction during the swing phase are included in the explanatory variables. The data acquisition unit acquires feature values from the mid-stance phase of gait waveform data of lateral acceleration. For example, the data acquisition unit acquires a feature value of the final part of the end-swing phase of gait waveform data of vertical acceleration. For example, the data acquisition unit acquires a feature value of the early part of the swing phase of gait waveform data of angular velocity in the coronal plane. For example, the data acquisition unit acquires a feature value of the mid-stance phase and the early swing phase of gait waveform data of angular velocity in the horizontal plane. For example, the data acquisition unit acquires a feature value of the timing of heel strike, which switches from the end-swing phase to the load response phase, of gait waveform data of angles in the horizontal plane. For example, the data acquisition unit acquires a feature value related to the amount of circumflex movement in the swing phase. The estimation unit inputs the acquired feature value data into an estimation model. The estimation unit estimates the user's static balance based on the user's static balance index output from the estimation model. According to this aspect, static balance that is more suited to physical activity can be estimated by using an estimation model that has learned features extracted from gait waveform data that includes features corresponding to muscle activity that affects static balance.

[0096] In one aspect of the present embodiment, the static balance estimation device is implemented in a terminal device having a screen viewable by a user. For example, the static balance estimation device displays information related to static balance estimated in accordance with the user's foot movement on the screen of the terminal device. For example, the static balance estimation device displays recommendation information corresponding to the static balance estimated in accordance with the user's foot movement on the screen of the terminal device. For example, a video related to training for strengthening a body part related to static balance is displayed on the screen of the terminal device as recommendation information corresponding to the static balance estimated in accordance with the user's foot movement. According to this aspect, by displaying the static balance estimated in accordance with the user's gait characteristics on a screen viewable by the user, the user can check information corresponding to their own static balance.

[0097] (Second embodiment) Next, a learning system according to a second embodiment will be described with reference to the drawings. The learning system of this embodiment generates an estimation model for estimating static balance in response to input of feature amounts by learning using feature amount data extracted from sensor data measured by a gait measurement device.

[0098] (composition) FIG. 27 is a block diagram showing an example of the configuration of learning system 2 according to this embodiment. Learning system 2 includes gait measurement device 20 and learning device 25. Gait measurement device 20 and learning device 25 may be connected by wire or wirelessly. Gait measurement device 20 and learning device 25 may be configured as a single device. Alternatively, gait measurement device 20 may be removed from the configuration of learning system 2, and learning system 2 may be configured with only learning device 25. Although FIG. 27 shows only one gait measurement device 20, one gait measurement device 20 may be provided for each foot (two in total). Alternatively, learning device 25 may be configured to perform learning without being connected to gait measurement device 20, using feature data that has been generated in advance by gait measurement device 20 and stored in a database.

[0099] The gait measurement device 20 is installed on at least one of the left and right feet. The gait measurement device 20 has the same configuration as the gait measurement device 10 of the first embodiment. The gait measurement device 20 includes an acceleration sensor and an angular velocity sensor. The gait measurement device 20 converts the measured physical quantity into digital data (also referred to as sensor data). The gait measurement device 20 generates gait waveform data for one normalized walking cycle from the time-series data of the sensor data. The gait measurement device 20 generates feature amount data used for estimating static balance. The gait measurement device 20 transmits the generated feature amount data to the learning device 25. Note that the gait measurement device 20 may be configured to transmit the feature amount data to a database (not shown) accessed by the learning device 25. The feature amount data stored in the database is used for learning by the learning device 25.

[0100] The learning device 25 receives the feature amount data from the gait measurement device 20. When using the feature amount data stored in a database (not shown), the learning device 25 receives the feature amount data from the database. The learning device 25 executes learning using the received feature amount data. For example, the learning device 25 learns teacher data with the feature amount data extracted from the walking waveform data of a plurality of subjects as explanatory variables and the value regarding static balance corresponding to the feature amount data as objective variables. There is no particular limitation on the learning algorithm executed by the learning device 25. The learning device 25 generates an estimation model learned using teacher data regarding a plurality of subjects. The learning device 25 stores the generated estimation model. The estimation model learned by the learning device 25 may be stored in a storage device external to the learning device 25.

[0101] 〔Learning Device〕 Next, the details of the learning device 25 will be described with reference to the drawings. FIG. 28 is a block diagram showing an example of the detailed configuration of the learning device 25. The learning device 25 includes a reception unit 251, a learning unit 253, and a storage unit 255.

[0102] The receiving unit 251 receives the feature amount data from the gait measurement device 20. The receiving unit 251 outputs the received feature amount data to the learning unit 253. The receiving unit 251 may receive the feature amount data from the gait measurement device 20 via a wired connection such as a cable, or may receive the feature amount data from the gait measurement device 20 via wireless communication. For example, the receiving unit 251 is configured to receive the feature amount data from the gait measurement device 20 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the receiving unit 251 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0103] The learning unit 253 acquires the feature amount data from the receiving unit 251. The learning unit 253 performs learning using the acquired feature amount data. For example, the learning unit 253 uses, as an explanatory variable, the feature amount data extracted regarding the gait of a subject, and uses, as an objective variable, the single-leg standing time of the subject, and learns a data set as teacher data. For example, the learning unit 253 generates an estimation model that estimates the single-leg standing time in response to the input of the feature amount data, learned for a plurality of subjects. For example, the learning unit 253 generates an estimation model according to the attribute data (age, height). For example, the learning unit 253 uses, as explanatory variables, the feature amount data extracted regarding the gait of a subject and the attribute data (age, height) of the subject, and generates an estimation model that estimates the single-leg standing time as a static balance. The learning unit 253 stores the estimation model learned for a plurality of subjects in the storage unit 255.

[0104] For example, the learning unit 253 executes learning using a linear regression algorithm. For example, the learning unit 253 executes learning using a support vector machine (SVM) algorithm. For example, the learning unit 253 executes learning using a Gaussian process regression (GPR) algorithm. For example, the learning unit 253 executes learning using a random forest (RF) algorithm. For example, the learning unit 253 may execute unsupervised learning for classifying the subject who is the source of the feature data according to the feature data. The learning algorithm executed by the learning unit 253 is not particularly limited.

[0105] The learning unit 253 may execute learning using the gait waveform data for one gait cycle as an explanatory variable. For example, the learning unit 253 executes supervised learning using the gait waveform data of the acceleration in three axial directions, the angular velocity around three axes, and the angle (posture angle) around three axes as explanatory variables and the correct value of the static balance index as the target variable. For example, when the gait phase is set in 1% increments in the gait cycle of 0 to 100%, the learning unit 253 learns using 909 explanatory variables.

[0106] FIG. 29 is a conceptual diagram for explaining learning for generating an estimation model. FIG. 29 is a conceptual diagram showing an example in which a learning unit 253 is made to learn using a data set of feature amounts F1 to F7 as explanatory variables and the single-leg standing time (static balance index) as the target variable as teacher data. For example, the learning unit 253 learns data regarding a plurality of subjects and generates an estimation model that outputs an output (estimated value) regarding the single-leg standing time (static balance index) in response to an input of a feature amount extracted from sensor data.

[0107] The storage unit 255 stores the estimation models learned for a plurality of subjects. For example, the storage unit 255 stores an estimation model for estimating static balance learned for a plurality of subjects. For example, the estimation model stored in the storage unit 255 is used for estimating the static balance by the static balance estimation device 13 of the first embodiment.

[0108] As described above, the learning system of the present embodiment includes a gait measurement device and a learning device. The gait measurement device acquires time-series data of sensor data regarding the movement of the feet. The gait measurement device extracts gait waveform data for one gait cycle from the time-series data of the sensor data and normalizes the extracted gait waveform data. The gait measurement device extracts, from the normalized gait waveform data, feature quantities used for estimating the static balance of the user from a gait phase cluster composed of at least one temporally continuous gait phase. The gait measurement device generates feature quantity data including the extracted feature quantities. The gait measurement device outputs the generated feature quantity data to the learning device.

[0109] The learning device includes a reception unit, a learning unit, and a storage unit. The reception unit acquires the feature quantity data generated by the gait measurement device. The learning unit executes learning using the feature quantity data. The learning unit generates an estimation model that outputs static balance in response to the input of the feature quantities (second feature quantities) of the gait phase cluster extracted from the time-series data of the sensor data measured as the user walks. The estimation model generated by the learning unit is stored in the storage unit.

[0110] The learning system of the present embodiment generates an estimation model using the feature quantity data measured by the gait measurement device. Therefore, according to this aspect, it is possible to generate an estimation model that can appropriately estimate the static balance in daily life without using an instrument for measuring the static balance.

[0111] (Third Embodiment) Next, a static balance estimation device according to a third embodiment will be described with reference to the drawings. The static balance estimation device of this embodiment has a simplified configuration of the static balance estimation device included in the static balance estimation system of the first embodiment.

[0112] 30 is a block diagram showing an example of the configuration of the static balance estimation device 33 according to this embodiment. The static balance estimation device 33 includes a data acquisition unit 331, a storage unit 332, an estimation unit 333, and an output unit 335.

[0113] The data acquisition unit 331 acquires feature data including feature amounts extracted from the characteristics of the user's gait and used to estimate the user's static balance index. The storage unit 332 stores an estimation model that outputs a static balance index according to input of the feature data. The estimation unit 333 inputs the acquired feature data to the estimation model and estimates the user's static balance according to the static balance index output from the estimation model. The output unit 335 outputs information related to the estimated static balance.

[0114] As described above, in this embodiment, the static balance of a user is estimated using feature amounts extracted from the characteristics of the user's gait. Therefore, according to this embodiment, the static balance can be appropriately estimated in daily life without using any equipment for measuring static balance.

[0115] (Hardware) Here, a hardware configuration for executing control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 31 as an example. Note that the information processing device 90 in Fig. 31 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0116] As shown in FIG. 31, the information processing apparatus 90 includes a processor 91, a main memory device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In FIG. 31, the interface is abbreviated as I / F (Interface). The processor 91, the main memory device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be capable of data communication. Also, the processor 91, the main memory device 92, the auxiliary storage device 93, and the input / output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

[0117] The processor 91 expands a program stored in the auxiliary storage device 93 or the like into the main memory device 92. The processor 91 executes the program expanded in the main memory device 92. In the present embodiment, a configuration may be adopted in which a software program installed in the information processing apparatus 90 is used. The processor 91 executes the control and processing according to each embodiment.

[0118] The main memory device 92 has an area where a program is expanded. In the main memory device 92, a program stored in the auxiliary storage device 93 or the like is expanded by the processor 91. The main memory device 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory), for example. Also, as the main memory device 92, a non-volatile memory such as an MRAM (Magnetoresistive Random Access Memory) may be configured / added.

[0119] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or a flash memory. Note that it is also possible to adopt a configuration in which various data is stored in the main memory device 92 and the auxiliary storage device 93 is omitted.

[0120] The input / output interface 95 is an interface for connecting the information processing device 90 and peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices through networks such as the Internet and intranets based on standards and specifications. The input / output interface 95 and the communication interface 96 may be shared as interfaces for connecting to external devices.

[0121] Input devices such as a keyboard, a mouse, and a touch panel may be connected to the information processing device 90 as needed. Those input devices are used for inputting information and settings. When using a touch panel as an input device, the display screen of the display device may also serve as the interface of the input device. Data communication between the processor 91 and the input device may be mediated by the input / output interface 95.

[0122] In addition, the information processing device 90 may be equipped with a display device for displaying information. When equipped with a display device, it is preferable that the information processing device 90 is provided with a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0123] In addition, the information processing device 90 may be equipped with a drive device. The drive device mediates the reading of data and programs from the recording medium and the writing of the processing results of the information processing device 90 to the recording medium between the processor 91 and the recording medium (program recording medium). The drive device may be connected to the information processing device 90 via the input / output interface 95.

[0124] The above is an example of a hardware configuration for enabling the control and processing according to each embodiment of the present invention. Note that the hardware configuration in FIG. 31 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present invention. Also, a program for causing a computer to execute the control and processing according to each embodiment is included in the scope of the present invention. Further, a program recording medium recording the program according to each embodiment is included in the scope of the present invention. The recording medium can be realized by, for example, an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may be realized by a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. Also, the recording medium may be realized by a magnetic recording medium such as a flexible disk or other recording media. When the program executed by the processor is recorded on the recording medium, the recording medium corresponds to a program recording medium.

[0125] The components of each embodiment may be arbitrarily combined. Also, the components of each embodiment may be realized by software or by a circuit.

[0126] Although the present invention has been described with reference to the embodiments above, the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0127] Some or all of the above embodiments can be described as follows in the appended claims, but are not limited thereto. (Appended Claim 1) A data acquisition unit that acquires feature amount data including a feature amount used for estimating the static balance of the user, extracted from the characteristics of the user's gait; A storage unit that stores an estimation model that outputs a static balance index in response to the input of the feature amount data; An estimation unit that inputs the acquired feature amount data into the estimation model and estimates the static balance of the user according to the static balance index output from the estimation model; An output unit that outputs information regarding the estimated static balance of the user. A static balance estimation device comprising: (Appendix 2) The data acquisition unit The static balance estimation device according to Appendix 1, which acquires feature amount data including features used for estimating a performance value of a one-leg stance test as the static balance index, the feature amount data being extracted from gait waveform data generated using time-series data of the sensor data related to the movement of the foot. (Appendix 3) The storage unit Stores the estimation model generated by learning using teacher data in which features used for estimating the static balance index are explanatory variables and the static balance indexes of a plurality of the subjects are target variables, for a plurality of subjects; The estimation unit The static balance estimation device according to Appendix 2, which inputs the feature amount data acquired for the user into the estimation model and estimates the static balance of the user according to the static balance index of the user output from the estimation model. (Appendix 4) The storage unit Stores the estimation model learned using explanatory variables including attribute data including at least any one of the ages and heights of a plurality of the subjects; The estimation unit The static balance estimation device according to Appendix 3, which inputs the feature amount data and the attribute data regarding the user into the estimation model and estimates the static balance of the user according to the static balance index of the user output from the estimation model. (Appendix 5) The storage unit the estimation model is generated by learning using training data in which, with respect to the gait waveform data of the plurality of subjects, a feature amount related to the activity of the gluteus medius muscle extracted from the final stage of the final swing phase and the mid-stance phase, a feature amount related to the activity of the adductor longus muscle and the sartorius muscle extracted from the early swing phase and the early swing phase, and a feature amount related to the activity of the abductor and adductor muscles in the swing phase are used as explanatory variables, and the static balance index of the plurality of subjects is used as a response variable; and The estimation unit 5. The static balance estimation device according to claim 3, wherein the feature data acquired in response to the user's walking is input to the estimation model, and the static balance of the user is estimated in response to the static balance index of the user output from the estimation model. (Appendix 6) The storage unit the estimation model is generated by learning using training data in which, for a plurality of the subjects, explanatory variables are a feature extracted from the mid-stance phase of the gait waveform data of lateral acceleration, a feature extracted from the final stage of the end-swing phase of the gait waveform data of vertical acceleration, a feature extracted from the early swing phase of the gait waveform data of angular velocity in the coronal plane, a feature extracted from the mid-stance phase and early swing phase of the gait waveform data of angular velocity in the horizontal plane, and a feature related to the amount of circumduction in the swing phase, and the static balance index of the plurality of the subjects is used as a response variable; and The data acquisition unit Acquire the feature amount data extracted in response to the user's walking, including a feature amount of the lateral acceleration in the mid-stance phase of the walking waveform data, a feature amount of the vertical acceleration in the final stage of the swing phase of the walking waveform data, a feature amount of the angular velocity in the coronal plane in the early swing phase of the walking waveform data, a feature amount of the angular velocity in the horizontal plane in the mid-stance phase and early swing phase of the walking waveform data, and a feature amount related to the amount of rotation in the swing phase; The estimation unit 6. The static balance estimation device according to claim 5, wherein the acquired feature data is input to the estimation model, and the static balance of the user is estimated according to the static balance index of the user output from the estimation model. (Appendix 7) The memory unit stores the estimation model generated by learning using, as explanatory variables, feature quantities related to the foot angle at the timing of heel contact when switching from the swing foot end to the load response period of the walking waveform data of the angle in the horizontal plane for a plurality of the subjects, and, as the objective variable, the static balance index of the plurality of the subjects. The data acquisition unit acquires the feature quantity data including the feature quantity of the foot angle at the timing of heel contact when switching from the swing foot end to the load response period of the walking waveform data of the angle in the horizontal plane. The estimation unit inputs the acquired feature quantity data into the estimation model, and estimates the static balance of the user according to the static balance index of the user output from the estimation model, which is the static balance estimation device described in Appendix 6. (Appendix 8) The estimation unit estimates information regarding the static balance of the user according to the static balance index estimated for the user. The output unit outputs the estimated information regarding the static balance, which is the static balance estimation device described in any one of Appendices 3 to 7. (Appendix 9) The static balance estimation device according to any one of Appendices 1 to 8, and a feature data generation unit that acquires time-series data of the sensor data including gait features, extracts gait waveform data for one step from the time-series data of the sensor data, normalizes the extracted gait waveform data, extracts feature data to be used for estimating the static balance from a gait phase cluster formed by at least one temporally consecutive gait phase, generates feature data including the extracted feature data, and outputs the generated feature data to the static balance estimation device. (Appendix 10) The static balance estimation device includes: implemented in a terminal device having a screen viewable by the user, 10. The static balance estimation system according to claim 9, wherein information about the static balance estimated in accordance with the movement of the user's feet is displayed on a screen of the terminal device. (Appendix 11) The static balance estimation device includes: 11. The static balance estimation system according to claim 10, wherein recommendation information according to the static balance estimated according to the foot movement of the user is displayed on a screen of the terminal device. (Appendix 12) The static balance estimation device includes: 12. The static balance estimation system according to claim 11, wherein a video relating to training for strengthening a body part related to the static balance is displayed on a screen of the terminal device as the recommendation information according to the static balance estimated according to the movement of the user's feet. (Appendix 13) The computer acquiring feature amount data including feature amounts used to estimate the static balance of the user, the feature amounts being extracted from the gait characteristics of the user; Input the obtained feature quantity data into an estimation model that outputs a static balance index corresponding to the input of the feature quantity data, Estimate the static balance of the user according to the static balance index output from the estimation model, A static balance estimation method for outputting information regarding the estimated static balance of the user. (Appendix 14) A process of obtaining feature quantity data including feature quantities used for estimating the static balance of the user, extracted from the characteristics of the user's gait, A process of inputting the obtained feature quantity data into an estimation model that outputs a static balance index corresponding to the input of the feature quantity data, A process of estimating the static balance of the user according to the static balance index output from the estimation model, A program for causing a computer to execute a process of outputting information regarding the estimated static balance of the user.

Explanation of Signs

[0128] 1 Static balance estimation system 2 Learning system 10, 20 Gait measurement device 11 Sensor 12 Feature quantity data generation unit 13 Static balance estimation device 25 Learning device 111 Acceleration sensor 112 Angular velocity sensor 121 Acquisition unit 122 Normalization unit 123 Extraction unit 125 Generation unit 127 Feature quantity data output unit 131, 331 Data acquisition unit 132, 332 Storage unit 133, 333 Estimation unit 135, 335 Output unit 251 Reception unit 253 Learning unit 255 Storage unit

Claims

1. Data acquisition means for acquiring feature amount data including a feature amount used for estimating a single-leg standing time correlated with the static balance of the user, the feature amount being extracted from walking waveform data generated using time-series data of sensor data measured according to the movement of the user's feet; Storage means for storing an estimation model learned to output the single-leg standing time in response to an input of the feature amount data; Estimation means for inputting the acquired feature amount data into the estimation model and estimating the static balance of the user according to the single-leg standing time output from the estimation model; Output means for outputting information regarding the estimated static balance of the user, comprising: The storage means: Regarding the walking waveform data of a plurality of subjects, stores the estimation model generated by learning using, as explanatory variables, feature amounts related to the activity of the gluteus medius muscle extracted from the end stage of the swing foot final stage and the midstance of the stance leg, feature amounts related to the activity of the tensor fasciae latae muscle and the sartorius muscle extracted from the early swing foot stage and the initial swing foot stage, and feature amounts related to the activity of the abductor and adductor muscle groups during the swing phase, and using, as the objective variable, the single-leg standing time of the plurality of subjects; The estimation means: A static balance estimation device that inputs the feature amount data acquired according to the walking of the user into the estimation model and estimates the static balance of the user according to the single-leg standing time of the user output from the estimation model.

2. The storage means: Regarding a plurality of subjects, stores the estimation model generated by learning using, as explanatory variables, feature amounts used for estimating the single-leg standing time, and using, as the objective variable, the single-leg standing time of the plurality of subjects; The estimation means: The static balance estimation device according to claim 1, which inputs the feature amount data acquired regarding the user into the estimation model and estimates the static balance of the user according to the single-leg standing time of the user output from the estimation model.

3. The storage means: For a plurality of the subjects, using as explanatory variables the feature quantities extracted from the mid-stance phase of the walking waveform data of the lateral acceleration, the feature quantities extracted from the end stage of the swing foot end of the walking waveform data of the vertical acceleration, the feature quantities extracted from the initial stage of the swing foot of the walking waveform data of the angular velocity in the coronal plane, the feature quantities extracted from the mid-stance phase and the pre-swing phase of the walking waveform data of the angular velocity in the horizontal plane, and the feature quantities related to the amount of circumduction in the swing phase, and using the single-leg stance time of the plurality of the subjects as the objective variable, the estimation model generated by learning using the teacher data is stored. The data acquisition means acquires the feature quantity data including the feature quantity of the mid-stance phase of the walking waveform data of the lateral acceleration, the feature quantity of the end stage of the swing foot end of the walking waveform data of the vertical acceleration, the feature quantity of the initial stage of the swing foot of the walking waveform data of the angular velocity in the coronal plane, the feature quantity of the mid-stance phase and the pre-swing phase of the walking waveform data of the angular velocity in the horizontal plane, and the feature quantity related to the amount of circumduction in the swing phase, which are extracted according to the walking of the user. The estimation means inputs the acquired feature quantity data into the estimation model, and estimates the static balance of the user according to the single-leg stance time of the user output from the estimation model. The static balance estimation device according to claim 1.

4. The storage means stores, for a plurality of the subjects, the estimation model generated by learning using as explanatory variables the feature quantities related to the foot angle at the timing of heel contact when switching from the swing foot end to the load response period of the walking waveform data of the angle in the horizontal plane, and using the single-leg stance time of the plurality of the subjects as the objective variable. The data acquisition means acquires the feature quantity data including the feature quantity of the foot angle at the timing of heel contact when switching from the swing foot end to the load response period of the walking waveform data of the angle in the horizontal plane. The estimation means inputs the acquired feature quantity data into the estimation model, and estimates the static balance of the user according to the single-leg stance time of the user output from the estimation model. The static balance estimation device according to claim 3.

5. The static balance estimation device according to any one of claims 1 to 4, and It is installed on the footwear of the user whose static balance is to be estimated, measures the spatial acceleration and the spatial angular velocity, generates sensor data related to the movement of the foot using the measured spatial acceleration and the spatial angular velocity, and outputs the generated sensor data; a feature quantity data generation means that acquires time-series data of the sensor data including the characteristics of the gait, extracts gait waveform data for one gait cycle from the time-series data of the sensor data, normalizes the extracted gait waveform data, and extracts a feature quantity used for estimating the single-leg stance time from the normalized gait waveform data from a gait phase cluster composed of at least one temporally continuous gait phase, generates feature quantity data including the extracted feature quantity, and outputs the generated feature quantity data to the static balance estimation device; A static balance estimation system comprising a gait measurement device having the above.

6. The static balance estimation device is mounted on a terminal device having a screen visible to the user, The static balance estimation system according to claim 5, wherein information regarding the static balance estimated according to the movement of the user's foot is displayed on the screen of the terminal device.

7. A computer acquires feature quantity data including a feature quantity used for estimating the single-leg stance time correlated with the static balance of the user, which is extracted from gait waveform data generated using time-series data of sensor data measured according to the movement of the user's foot, inputs the acquired feature quantity data into an estimation model trained to output the single-leg stance time in response to the input of the feature quantity data, estimates the static balance of the user according to the single-leg stance time output from the estimation model, outputs information regarding the estimated static balance of the user, In the estimation, Regarding the gait waveform data of a plurality of subjects, feature quantities related to the activity of the gluteus medius muscle extracted from the end stage of the swing leg end and the mid-stance of the stance leg, feature quantities related to the activity of the long adductor muscle and the sartorius muscle extracted from the early swing leg and the initial swing leg, and feature quantities related to the activity of the abductor and adductor muscle groups in the swing phase are used as explanatory variables, and the feature quantity data acquired according to the gait of the user is input into the estimation model generated by learning using teacher data having the single-leg stance time of the plurality of subjects as the target variable. A static balance estimation method for estimating the static balance of the user according to the single-leg standing time of the user output from the estimation model. **Claim 8**: A computer A process of obtaining feature quantity data including feature quantities used for estimating the single-leg standing time correlated with the static balance of the user, which are extracted from gait waveform data generated using time-series data of sensor data measured according to the movement of the user's feet. A process of inputting the obtained feature quantity data into an estimation model trained to output the single-leg standing time according to the input of the feature quantity data. A process of estimating the static balance of the user according to the single-leg standing time output from the estimation model. A process of outputting information regarding the estimated static balance of the user. In the process of estimating, For the gait waveform data of a plurality of subjects, a process of inputting the feature quantity data obtained according to the user's gait into the estimation model generated by learning using, as explanatory variables, feature quantities related to the activity of the gluteus medius muscle extracted from the end stage of the swing foot and the midstance of the stance leg, feature quantities related to the activity of the tensor fasciae latae muscle and the sartorius muscle extracted from the early swing phase and the initial swing phase, and feature quantities related to the activity of the abductor and adductor muscle groups in the swing phase, and using, as the objective variable, the single-leg standing time of the plurality of subjects. A program for causing a computer to execute a process of estimating the static balance of the user according to the single-leg standing time of the user output from the estimation model, and a process of estimating the static balance of the user according to the single-leg standing time of the user output from the estimation model.

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