Dynamic balance estimation device, dynamic balance estimation system, dynamic balance estimation method, and program

Abstract

In order to estimate dynamic balance in daily life, as appropriate, a dynamic balance estimation device according to the present invention is provided with: a data acquisition unit that acquires feature data including a feature to be used for estimation of the dynamic balance of a user, the feature data having been extracted from sensor data related to a foot movement of a user; a storage unit that stores an estimation model that outputs a dynamic balance index corresponding to the input of the feature quantity data; an estimation unit that inputs the acquired feature data to the estimation model and estimates the dynamic balance of the user according to the dynamic balance index output from the estimation model; and an output unit that outputs information related to the estimated dynamic balance of the user.

Description

[Technical Field]

[0001] The present disclosure relates to a dynamic balance estimation device and the like that estimates dynamic balance using sensor data related to foot movement. [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 a device for detecting foot abnormalities based on the walking characteristics of a walker. The device in Patent Document 1 uses data acquired from a sensor attached to the footwear to extract gait features that are characteristic of the gait of a walker wearing the footwear. The device in Patent Document 1 detects abnormalities in a walker wearing the footwear based on the extracted gait features. For example, the device in Patent Document 1 extracts characteristic parts related to hallux valgus from gait waveform data for one stride cycle. The device in Patent Document 1 estimates the progression of hallux valgus using the gait features of the extracted characteristic parts.

[0004] Patent Document 2 discloses an evaluation method for uniformly collecting data on multiple users from information obtained from the users' daily walking movements. The evaluation method of Patent Document 2 uses sensors attached to the insoles of shoes used by multiple users to acquire plantar pressure data for a predetermined period of time while walking and while standing still. The evaluation method of Patent Document 2 analyzes the acquired data and acquires and stores plantar pressure parameters, foot pressure center parameters, and time parameters while walking, and plantar pressure parameters and foot pressure center parameters while standing still, for each user. Patent Document 2 discloses that user data containing items (attributes) such as gender, date of birth, height, and weight is stored in a database.

[0005] Dynamic balance ability, which corresponds to the ability to maintain balance in the face of sudden disturbances, is important for maintaining dynamic stability during walking. Dynamic balance ability is an important indicator for assessing frailty and the risk of falling. Dynamic balance ability can be evaluated by the performance of the Functional Reach Test (FRT). The performance of the FRT is evaluated by measuring the distance between the fingertips (also called the FR distance) when the upper limbs are moved (reached) as far forward as possible with both hands raised 90 degrees to the horizontal.

[0006] Non-Patent Document 1 reports verification results showing increased muscle activity in the multifidus, long head of the biceps femoris, soleus, and medial head of the flexor hallucis brevis during functional reaching movements. Non-Patent Document 2 shows results showing a correlation between hip abductor strength and FR distance. Non-Patent Document 3 reports that verification of multi-directional FRT revealed a high correlation between iliopsoas muscle strength and FR distance. Non-Patent Document 4 discloses the idea that stability is achieved by changing the foot angle as a compensatory mechanism for the decline in balance ability and muscle function that occurs with aging. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2021 / 140658 [Patent Document 2] Patent Publication No. 2021-166898 [Non-patent literature]

[0008] [Non-Patent Document 1] Kentaro Sasaki et al., "Electromyographic Analysis of Functional Reach Movements," Physical Therapy Science, Vol. 24 (6), pp.813-816, 2009. [Non-patent document 2] Hirano, T. et al., “Relationship between Muscle Strength, Skeletal Muscle Mass, and Physical Function in Community-Dwelling Elderly People,” Nagoya Gakuin University Journal of Medicine, Health Science, and Sports Science, Vol. 4 (2), pp. 23-33, 2016. [Non-patent document 3] Akira Ono et al., "Relationship between lower limb muscle strength and multidirectional functional reach in the elderly," Physical Education Measurement and Evaluation Research, Vol.1, pp.119-126, 2001. [Non-patent document 4] Yasuhiro Shirao, “Relationship between foot angle and lower limb torsion angle during walking,” Physical Therapy Supplement, Vol. 41 Suppl. No. 2 (Abstracts of the 49th Japanese Physical Therapy Congress), pp. 0433, 2014. Summary of the Invention [Problem to be solved by the invention]

[0009] The method of Patent Document 1 estimates the progression of hallux valgus using gait feature quantities of characteristic parts extracted from data acquired by a sensor attached to footwear. Patent Document 1 does not disclose estimating dynamic balance using gait feature quantities of characteristic parts extracted from data acquired by a sensor attached to footwear.

[0010] The method of Patent Document 2 uses data on plantar pressure measured over a predetermined period of time by a sensor attached to the insole to acquire and store parameters such as plantar pressure parameters, center of foot pressure parameters, and time parameters during walking. The method of Patent Document 2 estimates the user's physical condition by analyzing the stored parameters. Patent Document 2 discloses that attributes such as gender, date of birth, height, and weight are stored, but does not disclose specific uses.

[0011] Dynamic balance can be evaluated if the FRT performance 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 dynamic balance such as FRT in daily life.

[0012] An object of the present disclosure is to provide a dynamic balance estimation device and the like that can appropriately estimate dynamic balance in daily life. [Means for solving the problem]

[0013] A dynamic balance estimation device according to one aspect of the present disclosure includes a data acquisition unit that acquires feature data including features used to estimate a user's dynamic balance, extracted from sensor data relating to the movement of the user's feet; a memory unit that stores an estimation model that outputs a dynamic balance index according to the input of the feature data; an estimation unit that inputs the acquired feature data into the estimation model and estimates the user's dynamic balance according to the dynamic balance index output from the estimation model; and an output unit that outputs information relating to the estimated dynamic balance of the user.

[0014] In one aspect of the dynamic balance estimation method of the present disclosure, feature data including features used to estimate the user's dynamic balance is acquired from sensor data related to the user's foot movement, the acquired feature data is input to an estimation model that outputs a dynamic balance index according to the input feature data, the user's dynamic balance is estimated according to the dynamic balance index output from the estimation model, and information related to the estimated user's dynamic balance is output.

[0015] A program according to one aspect of the present disclosure causes a computer to perform the following processes: acquiring feature data including features used to estimate a user's dynamic balance, extracted from sensor data relating to the movement of the user's feet; inputting the acquired feature data into an estimation model that outputs a dynamic balance index according to the input of the feature data; estimating the user's dynamic balance according to the dynamic balance index output from the estimation model; and outputting information relating to the estimated dynamic balance of the user. [Effects of the Invention]

[0016] According to the present disclosure, it is possible to provide a dynamic balance estimation device or the like that can appropriately estimate dynamic balance in daily life. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a block diagram showing an example of the configuration of a dynamic balance estimation system according to a first embodiment. [Figure 2] 1 is a block diagram showing an example of the configuration of a gait measurement device included in the dynamic 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 dynamic balance estimation device included in a dynamic balance estimation system according to a first embodiment. [Figure 12] FIG. 2 is a conceptual diagram for explaining a functional reach distance estimated by the dynamic balance estimation system according to the first embodiment. [Figure 13] FIG. 2 is a conceptual diagram for explaining a functional reach distance estimated by the dynamic balance estimation system according to the first embodiment. [Figure 14] 1 is a table showing specific examples of feature amounts extracted for estimating functional reach distance by a gait measurement device included in the dynamic balance estimation system according to the first embodiment. [Figure 15] 10 is a graph showing the correlation between a feature value F1 extracted by a gait measurement device included in the dynamic balance estimation system according to the first embodiment and an actually measured functional reach distance. [Figure 16] 10 is a graph showing the correlation between a feature value F2 extracted by the gait measurement device included in the dynamic balance estimation system according to the first embodiment and an actually measured functional reach distance. [Figure 17] 10 is a graph showing the correlation between a feature value F3 extracted by the gait measurement device included in the dynamic balance estimation system according to the first embodiment and an actually measured functional reach distance. [Figure 18]10 is a graph showing the correlation between a feature value F4 extracted by the gait measurement device included in the dynamic balance estimation system according to the first embodiment and an actually measured functional reach distance. [Figure 19] 10 is a graph showing the correlation between a feature F5 extracted by the gait measurement device included in the dynamic balance estimation system according to the first embodiment and an actually measured functional reach distance. [Figure 20] FIG. 2 is a block diagram showing an example of estimation of a functional reach distance (dynamic balance index) by a dynamic balance estimation device included in the dynamic balance estimation system according to the first embodiment. [Figure 21] This graph shows the correlation between the estimated functional reach distance, estimated using an estimation model generated by learning with gender, age, height, weight, and walking speed as explanatory variables, and the measured functional reach distance. [Figure 22] 1 is a graph showing the correlation between the estimated value of functional reach distance estimated by the dynamic balance estimation device included in the dynamic balance estimation system according to the first embodiment and the measured value of functional reach distance. [Figure 23] 5 is a flowchart for explaining an example of the operation of the gait measurement device included in the dynamic balance estimation system according to the first embodiment. [Figure 24] 5 is a flowchart for explaining an example of an operation of the dynamic balance estimation device included in the dynamic balance estimation system according to the first embodiment. [Figure 25] FIG. 2 is a conceptual diagram for explaining an application example of the dynamic balance estimation system according to the first embodiment. [Figure 26] FIG. 10 is a block diagram showing an example of the configuration of a learning system according to a second embodiment. [Figure 27] 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 28] 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 29] FIG. 10 is a block diagram showing an example of the configuration of a dynamic balance estimation device according to a third embodiment. [Figure 30] 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

[0018] 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.

[0019] (First embodiment) First, a dynamic balance estimation system according to a first embodiment will be described with reference to the drawings. The dynamic balance estimation system of this embodiment measures sensor data related to foot movements according to a user's walking. The dynamic balance estimation system of this embodiment estimates the dynamic balance of the user using the measured sensor data.

[0020] In this embodiment, an example of dynamic balance is estimating the performance of a Functional Reach Test (FRT). In this embodiment, the performance of the FRT is evaluated based on the distance between the fingertips (also referred to as the functional reach distance) when the upper limbs are moved (reached) as far forward as possible from a standing position with both hands raised 90 degrees relative to the horizontal. The functional reach distance (hereinafter referred to as the FR distance) is the performance value of the FRT. The larger the FR distance, the higher the performance of the FRT. The method of this embodiment can be applied to tests other than the FRT performed with both hands. For example, the method of this embodiment can also be applied to the FRT performed with one hand and other variations of the FRT.

[0021] (composition) FIG. 1 is a block diagram showing an example of the configuration of a dynamic balance estimation system 1 according to this embodiment. The dynamic balance estimation system 1 includes a gait measurement device 10 and a dynamic balance estimation device 13. In this embodiment, an example will be described in which the gait measurement device 10 and the dynamic balance estimation device 13 are configured as separate pieces of hardware. For example, the gait measurement device 10 is attached to the footwear of a subject (user) whose dynamic balance is to be estimated. For example, the functions of the dynamic balance estimation device 13 are installed in a mobile device carried by the subject (user). Below, the configurations of the gait measurement device 10 and the dynamic balance estimation device 13 will be described separately.

[0022] [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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] The sensor 11 is realized by, for example, an inertial measurement unit that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). 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. The sensor 11 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading). The sensor 11 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). The sensor 11 may be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement.

[0027] FIG. 3 is a conceptual diagram showing an example in which gait measurement device 10 is placed inside shoe 100 of a right foot. In the example of FIG. 3, gait measurement device 10 is installed at a position corresponding to the back of the arch of the foot. For example, gait measurement device 10 is placed in an insole inserted into shoe 100. For example, gait measurement device 10 may be placed on the bottom of shoe 100. For example, gait measurement device 10 may be embedded in the body of shoe 100. gait measurement device 10 may or may not be detachable from shoe 100. gait measurement device 10 may be installed at a position other than the back of the arch of the foot as long as it can measure sensor data related to foot movement. gait measurement device 10 may also be installed in socks worn by the user or in an accessory such as an anklet worn by the user. gait measurement device 10 may also be attached directly to the foot or embedded in the foot. 3 shows an example in which the gait measurement device 10 is installed in the right shoe 100. The gait measurement device 10 may also be installed in the shoes 100 on both feet.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] As shown in FIG. 2 , feature data generation unit 12 (also referred to as a feature data generation device) includes an acquisition unit 121, a normalization unit 122, an extraction unit 123, a generation unit 125, and a feature data output unit 127. For example, feature data generation unit 12 is implemented by a microcomputer or microcontroller that performs overall control of gait measurement device 10 and data processing. For example, feature data generation unit 12 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc. The feature data generation unit 12 controls an acceleration sensor 111 and an angular velocity sensor 112 to measure angular velocity and acceleration. For example, feature data generation unit 12 may be implemented on the side of a mobile terminal (not shown) carried by the subject (user).

[0032] The acquiring unit 121 acquires acceleration in three axial directions from the acceleration sensor 111. The acquiring unit 121 also acquires angular velocities around three axes from the angular velocity sensor 112. For example, the acquiring unit 121 performs analog-to-digital conversion (AD conversion) on physical quantities (analog data) such as the acquired angular velocities and accelerations. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data by the acceleration sensor 111 and the angular velocity sensor 112, respectively. The acquiring unit 121 outputs the converted digital data (also referred to as sensor data) to the normalizing unit 122. The acquiring unit 121 may be configured to store the sensor data in a storage unit (not shown). The sensor data includes at least acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in the three axial directions. The angular velocity data includes angular velocity vectors around three axes. The acceleration data and angular velocity data are associated with the time when the data was acquired. The acquiring unit 121 may also apply corrections to the acceleration data and angular velocity data, such as correction for mounting error, temperature correction, and linearity correction.

[0033] 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.

[0034] 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.

[0035] As shown in Figure 6, multiple events (also called walking events) occur during walking. E1 represents the event in which the heel of the right foot touches the ground (heel contact: HC). E2 represents the event in which the toe of the left foot leaves the ground (opposite toe off: OTO) while the sole of the right foot is in contact with the ground. E3 represents the event in which the heel of the right foot rises (heel rise: HR) while the sole of the right foot is in contact with the ground (opposite heel strike: OHS). E5 represents the event in which the toe of the right foot leaves the ground (toe off: TO) while the sole of the left foot is in contact with the ground (toe off: TO). E6 represents the event in which the left and right feet cross (foot crossing: FA) while the sole of the left foot is in contact with the ground (foot adjacent). E7 represents the event where the tibia of the right foot is nearly perpendicular to the ground (Tibia Vertical: TV) with the sole of the left foot touching the ground. E8 represents the event where the heel of the right foot touches the ground (Heel Contact: HC). E8 corresponds to the end point of the walking cycle that begins with E1 and also corresponds to the starting point of the next walking cycle. Note that Figure 6 is an example and does not limit the events that occur during walking or the names of those events.

[0036] FIG. 7 is a conceptual diagram for explaining an example of gait parameters. R , left foot step length S L , stride length T, step width W, and foot angle F are shown. Right foot step length S R is the difference in Y coordinate between the heel of the right foot and the heel of the left foot when the state transitions from the state where the sole of the left foot is on the ground to the state where the heel of the right foot, which is swung in the direction of travel, lands on the ground. Left foot step length S L is the difference in Y coordinate between the heel of the left foot and the heel of the right foot when the sole of the right foot is on the ground and the heel of the left foot is on the ground, swinging forward in the direction of travel. The stride length T is the difference between the right foot step length S R and left foot step length S LThe step width W is the distance between the right foot and the left foot. In FIG. 7, the 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. The foot angle F is the angle formed 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 during the swing phase when the foot is in the air.

[0037] 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.

[0038] 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%.

[0039] In the example of FIG. 9, the normalization unit 122 normalizes the section from heel-strike HC, where the walking phase is 0%, to toe-off TO, which follows the heel-strike HC, to 0-60%. The normalization unit 122 also normalizes the section from toe-off TO to heel-strike HC, where the walking phase is 100%, to 60-100%. As a result, the gait waveform data for one step cycle is normalized into a section where the gait cycle is 0-60% (stance phase) and a section where the gait cycle is 60-100% (swing phase). In FIG. 9, the gait waveform data after the second normalization is shown by the solid line. In the gait waveform data after the second normalization (solid line), the timing of toe-off TO coincides with 60%.

[0040] 8 and 9 show an example in which gait waveform data for one step cycle is extracted and normalized based on the traveling acceleration (Y-direction acceleration). With respect to accelerations / angular velocities other than the traveling acceleration (Y-direction acceleration), the normalization unit 122 extracts and normalizes gait waveform data for one step cycle in accordance with the traveling acceleration (Y-direction acceleration) gait cycle. The normalization unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In this case, the normalization unit 122 extracts and normalizes gait waveform data for one step cycle in accordance with the traveling acceleration (Y-direction acceleration) gait cycle, also with respect to angles around three axes.

[0041] 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).

[0042] 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 dynamic balance from the walking waveform data for one step gait cycle. The extraction unit 123 extracts feature quantities for each walking phase cluster from walking phase clusters that integrate 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 dynamic balance are extracted will be described later.

[0043] FIG. 10 is a conceptual diagram illustrating the extraction of feature quantities for estimating dynamic balance from gait waveform data for one step cycle. For example, the extraction unit 123 extracts temporally consecutive walking phases i to i+m as a walking phase cluster C (i and m are natural numbers). The walking phase cluster C includes m walking phases (components). That is, the number of walking phases (components) constituting the walking phase cluster C (also referred to as the number of components) is m. While FIG. 10 illustrates an example in which the walking phases are integer values, the walking phases may be subdivided to the nearest decimal point. When the walking phases are subdivided to the nearest decimal point, the number of components of the walking phase cluster C corresponds to the number of data points in the section of the walking phase cluster. The extraction unit 123 extracts feature quantities from each of the walking phases i to i+m. When the walking phase cluster C is composed of a single walking phase j, the extraction unit 123 extracts feature quantities from the single walking phase j (j is a natural number).

[0044] The generation unit 125 applies a feature composition formula to a feature (first feature) extracted from each of the walking phases constituting the walking phase cluster to generate a feature (second feature) of the walking phase cluster. The feature composition formula is a calculation formula set in advance for generating a feature of the walking phase cluster. For example, the feature composition formula is a calculation formula related to four arithmetic operations. For example, the second feature calculated using the feature composition formula is an integral average value, arithmetic mean value, slope, variance, etc. of the first feature in each walking phase included in the walking phase cluster. For example, the generation unit 125 applies, as the feature composition formula, a calculation formula for calculating the slope or variance of the first feature extracted from each of the walking phases constituting the walking phase cluster. For example, when a walking phase cluster is composed of a single walking phase, it is not possible to calculate the slope or variance, so a feature composition formula for calculating an integral average value, arithmetic mean value, etc. may be used.

[0045] 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 dynamic balance estimation device 13, which uses the feature data.

[0046] [Dynamic balance estimation device] 11 is a block diagram showing an example of the configuration of the dynamic balance estimation device 13. The dynamic balance estimation device 13 includes a data acquisition unit 131, a storage unit 132, an estimation unit 133, and an output unit 135.

[0047] 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).

[0048] The storage unit 132 stores an estimation model that estimates the FR distance as dynamic balance using feature amount data extracted from the walking waveform data. The storage unit 132 stores feature amount data related to the FR distance of multiple subjects and an estimation model that has learned the relationship with the FR distance. For example, the storage unit 132 stores an estimation model that estimates the FR distance that has been learned for multiple subjects. The FR distance is affected by height. Therefore, the storage unit 132 may store an estimation model according to attribute data related to height.

[0049] 12 and 13 are conceptual diagrams for explaining the FR distance. FIG. 12 is a conceptual diagram showing a state in which both hands are raised at 90 degrees relative to the horizontal plane. FIG. 12 shows position A of the fingertips of both raised hands. FIG. 13 is a conceptual diagram showing a state in which the upper limbs have been moved (reached) as far forward as possible from the state of FIG. 12. FIG. 13 shows position A of the fingertips in the state of FIG. 12, as well as position B of the fingertips in a state in which the upper limbs have been moved as far forward as possible. The distance d between position A and position B corresponds to the FR distance.

[0050] Dynamic balance can be evaluated according to the FR distance value. If the FR distance is 30 cm (centimeters) or more, the dynamic balance is high and the risk of falling is low. If the FR distance is within the range of 25 to 30 cm, the dynamic balance is average. If the FR distance is within the range of 20 to 25 cm, the dynamic balance is low and there is a risk of falling. If the FR distance is less than 20 cm, the dynamic balance is very low and there is a very high risk of falling. The dynamic balance evaluation criteria according to the FR distance listed here are only guidelines and may be set according to the situation. For example, the evaluation criteria for dynamic balance according to the FR distance value will differ depending on the subject's medical history.

[0051] The estimation model may be stored in the storage unit 132 at the time of shipping the product from the factory or at the time of calibration before the user uses the dynamic balance estimation system 1. For example, the system may be configured to use an estimation model stored in a storage device such as an external server. In this case, the estimation model may be used via an interface (not shown) connected to the storage device.

[0052] 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 FR distance as the dynamic 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 according to the dynamic balance (FR distance) 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.

[0053] The output unit 135 outputs the dynamic balance estimation result obtained by the estimation unit 133. For example, the output unit 135 displays the dynamic 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 dynamic balance output from the dynamic balance estimation device 13 can be used.

[0054] For example, the dynamic balance estimation device 13 is connected to an external system, such as a cloud or a server, via a mobile device (not shown) carried by the subject (user). The mobile device (not shown) is a portable communication device. Examples of the mobile device include a smartphone, a smart watch, a mobile phone, or other portable communication device with a communication function. For example, the dynamic balance estimation device 13 is connected to the mobile device via a wired connection such as a cable. For example, the dynamic balance estimation device 13 is connected to the mobile device via wireless communication. For example, the dynamic balance estimation device 13 is connected to the mobile device via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the dynamic balance estimation device 13 may also conform to standards other than Bluetooth (registered trademark) or WiFi (registered trademark). The dynamic balance estimation result may be used by an application installed on the mobile device. In this case, the mobile device executes processing using the estimation result using application software or the like installed on the mobile device.

[0055] [FR distance estimation] Next, the correlation between FR distance and feature data will be explained with a verification example. FIG. 14 is a correspondence table summarizing the feature values ​​used to estimate FR distance. The correspondence table in FIG. 14 associates the feature value number, the gait waveform data from which the feature value is extracted, the gait phase (%) from which the gait phase cluster is extracted, and the associated muscles. FR distance correlates with the activity of the gluteus medius, iliacus, hamstrings (long head of biceps femoris), tibialis anterior, etc., as well as the magnitude of the compensatory movement of turning the toes outward. Therefore, feature values ​​F1 to F5 extracted from the gait phases in which these features appear are used to estimate FR distance.

[0056] 15 to 19 show the results of verifying the correlation between the FR distance and feature amount data. Figures 15 to 19 show the results of verification conducted on a total of 62 subjects, consisting of 27 men and 35 women aged 60 to 85. Figures 15 to 19 also show the results of verifying the correlation between estimated values ​​estimated using feature amounts extracted from walking while wearing footwear equipped with the gait measurement device 10 and the measured values ​​(true values) of the FR distance.

[0057] The feature F1 is extracted from the 75-79% section of the walking phase of the walking waveform data Ay, which is related to the time-series data of forward acceleration (Y-direction acceleration). The 75-79% walking phase is included in the mid-swing phase T6. The feature F1 mainly includes features related to the movement of the tibialis anterior and the short head of the biceps femoris. Figure 15 shows the results of verifying the correlation between the feature F1 and the FR distance. The horizontal axis of the graph in Figure 15 represents normalized acceleration. The correlation coefficient R between the feature F1 and the FR distance was 0.343.

[0058] The feature F2 is extracted from the 62% section of the walking phase of the walking waveform data Az, which is related to the time-series data of vertical acceleration (Z-direction acceleration). The 62% walking phase is included in the initial swing phase T5. The feature F2 mainly includes features related to the movement of the iliacus muscle. Figure 16 shows the results of verifying the correlation between the feature F2 and the FR distance. The horizontal axis of the graph in Figure 16 represents normalized acceleration. The correlation coefficient R between the feature F2 and the FR distance was -0.321.

[0059] The feature F3 is extracted from the 7-8% section of the walking phase of the walking waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 7-8% walking phase is included in the load response period T1. The feature F3 mainly includes features related to the movement of the gluteus medius muscle. Figure 17 shows the results of verifying the correlation between the feature F3 and the FR distance. The horizontal axis of the graph in Figure 17 represents the angle in the coronal plane. The correlation coefficient R between the feature F3 and the FR distance was -0.349.

[0060] Feature F4 is extracted from the 57-58% section of the walking phase of the walking waveform data Ez, which is related to time-series data of angles (postural angles) in the horizontal plane (around the Z-axis). The 57-58% walking phase is included in the early swing phase T4. Feature F4 mainly includes features related to compensatory movements. Compensatory movements are movements that change the foot angle to achieve stability in order to compensate for the decline in balance ability and muscle function that occurs with aging. Figure 18 shows the results of verifying the correlation between feature F4 and FR distance. The horizontal axis of the graph in Figure 18 represents the angle in the horizontal plane (plantar angle). The correlation coefficient R between feature F4 and FR distance was -0.286.

[0061] The feature F5 is the average value of the foot angle in the horizontal plane during the swing phase. For example, the feature F5 is the average value of the gait waveform data Ez during the swing phase. In other words, the feature F5 is the integral value of the gait waveform data Gz related to the time series data of angular velocity in the horizontal plane (around the Z axis). The feature F5 mainly includes features related to compensatory movements. Compensatory movements are movements that change the foot angle to achieve stability in order to compensate for the decline in balance ability and muscle function that occurs with aging. Figure 19 shows the results of verifying the correlation between the feature F5 and the FR distance. The horizontal axis of the graph in Figure 19 represents the angle in the coronal plane. The correlation coefficient R between the feature F5 and the FR distance was -0.353.

[0062] FIG. 20 is a conceptual diagram showing an example in which an estimated value of the FR distance is output by inputting feature quantities F1 to F5 extracted from sensor data measured as the user walks into an estimation model 151 constructed in advance to estimate the FR distance as dynamic balance. The estimation model 151 outputs the FR distance, which is an index of dynamic balance, in response to the input feature quantities F1 to F5. For example, the estimation model 151 is generated by learning using training data in which the feature quantities F1 to F5 used to estimate the FR distance are used as explanatory variables and the FR distance is used as a target variable. There are no limitations on the estimation result of the estimation model 151 as long as an estimation result regarding the FR distance, which is an index of dynamic balance, is output in response to the input feature quantity data for estimating the FR distance. For example, the estimation model 151 may be a model that estimates the FR distance using attribute data (height) as an explanatory variable in addition to the feature quantities F1 to F5 used to estimate the FR distance.

[0063] For example, an estimation model for estimating the FR distance using a multiple regression prediction method is stored in the storage unit 132. For example, the storage unit 132 stores parameters for estimating the FR distance using the following equation 1. FR distance=a1×F1+a2×F2+a3×F3+a4×F4+a5×F5+a0...(1) In the above formula 1, F1, F2, F3, F4, and F5 are feature quantities for each walking phase cluster used to estimate the FR distance, as shown in the correspondence table in FIG. 14. a1, a2, a3, a4, and a5 are coefficients by which F1, F2, F3, F4, and F5 are multiplied. a0 is a constant term. For example, a0, a1, a2, a3, a4, and a5 are stored in the storage unit 132.

[0064] Next, we will show the results of evaluating the estimation model 151 generated using the measurement data of the 62 subjects described above. Here, we compare a verification example (Fig. 21) in which dynamic balance (FR distance) was estimated using the subject's attributes (including walking speed) with a verification example (Fig. 22) in which dynamic balance (FR distance) was estimated using the subject's gait features. Figs. 21 and 22 show the results of testing the estimation model generated using the measurement data of 61 subjects using the measurement data of the remaining subject using the LOSO (Leave-One-Subject-Out) method. Figs. 21 and 22 show the results of performing LOSO on all (62) subjects, and comparing the predicted values ​​obtained by the test with the measured values ​​(true values). The LOSO test results were calculated using the intraclass correlation coefficients (ICC), mean absolute error (MAE), and coefficient of determination (R). 2 The intraclass correlation coefficient (ICC) was used to evaluate inter-rater reliability.

[0065] Figure 21 shows the results of testing an estimation model of a comparative example that was trained using training data with gender, age, height, weight, and walking speed as explanatory variables and FR distance as the objective variable. The estimation model of the comparative example had an intraclass correlation coefficient ICC(2, 1) of 0.18, a mean absolute error MAE of 5.31, and a coefficient of determination R 2 was 0.06.

[0066] 22 shows the verification results of the estimation model 151 of this embodiment, which was trained using training data with the features F1 to F5, age, and height as explanatory variables and the FR distance as the objective variable. The estimation model 151 of this embodiment has an intraclass correlation coefficient ICC(2, 1) of 0.644, a mean absolute error MAE of 4.17, and a coefficient of determination R 2 was 0.44. That is, the estimation model 151 of this embodiment is more reliable, has a smaller error, and the dependent variable is sufficiently explained by the explanatory variables, compared to the estimation model of the comparative example. That is, according to the method of this embodiment, it is possible to generate an estimation model 151 that is more reliable, has a smaller error, and the dependent variable is sufficiently explained by the explanatory variables, compared to an estimation model that uses only attributes and walking speed.

[0067] (operation) Next, the operation of dynamic balance estimation system 1 will be described with reference to the drawings. Here, we will explain separately gait measurement device 10 and dynamic balance estimation device 13 included in dynamic 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.

[0068] [Gait measurement device] Fig. 23 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. 23, the feature amount data generation unit 12 will be described as the subject of the operation.

[0069] In FIG. 23, first, the feature amount data generator 12 acquires time-series data of sensor data relating to foot movements (step S101).

[0070] 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.

[0071] 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).

[0072] Next, the feature data generating unit 12 extracts feature values ​​from the normalized walking waveform, from the walking phases used for estimating dynamic balance (step S104). For example, the feature data generating unit 12 extracts feature values ​​to be input to a pre-constructed estimation model.

[0073] Next, the feature data generator 12 uses the extracted feature to generate a feature for each walking phase cluster (step S105).

[0074] 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).

[0075] Next, the feature amount data generating unit 12 outputs the generated feature amount data to the dynamic balance estimating device 13 (step S107).

[0076] [Dynamic balance estimation device] Fig. 24 is a flowchart for explaining the operation of the dynamic balance estimation device 13. In the explanation following the flowchart of Fig. 24, the dynamic balance estimation device 13 will be described as the subject of the operation.

[0077] In FIG. 24, first, the dynamic balance estimation device 13 acquires feature amount data generated using sensor data related to foot movements (step S131).

[0078] Next, the dynamic balance estimation device 13 inputs the acquired feature amount data into an estimation model for estimating the dynamic balance (FR distance) (step S132).

[0079] Next, the dynamic balance estimation device 13 estimates the dynamic balance of the user according to the output (estimated value) from the estimation model (step S133). For example, the dynamic balance estimation device 13 estimates the FR distance of the user as the dynamic balance.

[0080] Next, the dynamic balance estimation device 13 outputs information about the estimated dynamic balance (step S134). For example, the dynamic balance is output to a terminal device (not shown) carried by the user. For example, the dynamic balance is output to a system that executes processing using the dynamic balance.

[0081] (Application example) Next, application examples according to this embodiment will be described with reference to the drawings. In the following application examples, a function of a dynamic balance estimation device 13 installed on a mobile terminal carried by a user is shown to estimate information related to dynamic balance using feature data measured by a gait measurement device 10 placed on a shoe.

[0082] Fig. 25 is a conceptual diagram showing an example of displaying the estimation result by the dynamic balance estimation device 13 on the screen of a mobile terminal 160 carried by a user walking while wearing shoes 100 equipped with a gait measurement device 10. Fig. 25 shows an example of displaying information corresponding to the estimation result of dynamic balance using feature amount data corresponding to sensor data measured while the user was walking on the screen of the mobile terminal 160.

[0083] FIG. 25 shows an example in which information according to an estimated value of the FR distance, which is dynamic balance, is displayed on the screen of the mobile device 160. In the example of FIG. 25, the estimated value of the FR distance is displayed on the display unit of the mobile device 160 as the estimation result of dynamic balance. Also in the example of FIG. 25, information on the estimation result of dynamic balance, such as "Your dynamic balance has decreased," is displayed on the display unit of the mobile device 160 in accordance with the estimation value of the FR distance, which is dynamic balance. Also in the example of FIG. 25, recommendation information according to the estimation result of dynamic balance, such as "Training A is recommended. Please watch the video below," is displayed on the display unit of the mobile device 160 in accordance with the estimation value of the FR distance, which is dynamic balance. A user who has checked the information displayed on the display unit of the mobile device 160 can practice training that will lead to an improvement in dynamic balance by exercising while referring to the video of Training A in accordance with the recommendation information.

[0084] As described above, the dynamic balance estimation system of this embodiment includes a gait measurement device and a dynamic balance estimation device. The gait measurement device includes a sensor and a feature data generation unit. The sensor has an acceleration sensor and an angular velocity sensor. The sensor measures spatial acceleration using the acceleration sensor. The sensor measures spatial angular velocity using the angular velocity sensor. The sensor generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity. The sensor outputs the generated sensor data to the feature data generation unit. The feature data generation unit acquires time-series sensor data related to foot movement. The feature data generation unit extracts gait waveform data for one walking cycle from the time-series sensor data. The feature data generation unit normalizes the extracted gait waveform data. From the normalized gait waveform data, the feature data generation unit extracts feature values ​​used for dynamic balance estimation from a walking phase cluster composed of at least one temporally consecutive walking phase. The feature data generation unit generates feature data including the extracted feature values. The feature data generation unit outputs the generated feature data.

[0085] The dynamic 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 features used to estimate the user's dynamic balance, extracted from sensor data related to the user's foot movements. The storage unit stores an estimation model that outputs a dynamic 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 dynamic balance according to the dynamic balance index output from the estimation model. The output unit outputs information related to the estimated dynamic balance.

[0086] The dynamic balance estimation system of this embodiment estimates the dynamic balance of a user using feature quantities extracted from sensor data related to the movement of the user's feet. Therefore, the dynamic balance estimation system of this embodiment can appropriately estimate dynamic balance in daily life without using any equipment for measuring dynamic balance.

[0087] 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 (functional reach distance) of a functional reach test as a dynamic balance index. According to this aspect, by using the sensor data related to foot movement, dynamic balance can be appropriately estimated in daily life without using any equipment for measuring dynamic balance.

[0088] 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 dynamic balance indices are explanatory variables and the dynamic balance indices of 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 dynamic balance according to the user's dynamic balance indices output from the estimation model. According to this aspect, dynamic balance can be appropriately estimated in daily life without using any equipment for measuring dynamic balance.

[0089] In one aspect of this embodiment, the storage unit stores an estimation model trained using explanatory variables including the subject's attribute data (height). The estimation unit inputs feature data and attribute data (height) related to the user into the estimation model. The estimation unit estimates the user's dynamic balance according to the user's dynamic balance index output from the estimation model. In this aspect, dynamic balance is estimated including attribute data (height) that affects dynamic balance. Therefore, according to this aspect, dynamic balance can be measured with higher accuracy.

[0090] 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 dynamic balance indices of the multiple subjects are used as objective variables. For example, a feature quantity related to gluteus medius activity extracted from the load response phase is included in the explanatory variables. For example, a feature quantity related to iliacus activity extracted from the early swing phase is included in the explanatory variables. For example, a feature quantity related to tibialis anterior and short head of biceps femoris extracted from the mid-swing phase, and a feature quantity related to foot angle compensation 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 dynamic balance based on the user's dynamic balance indices output from the estimation model. According to this aspect, dynamic balance that is more suited to physical activity can be estimated using an estimation model that has learned feature quantities related to muscle activity that affects dynamic balance.

[0091] 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 quantities extracted from gait waveform data are used as explanatory variables and the subject's dynamic balance related to a dynamic balance index is used as a response variable. For example, the explanatory variables include feature quantities extracted from the load response phase of gait waveform data of angular velocity in the coronal plane. For example, the explanatory variables include feature quantities extracted from the early swing phase of gait waveform data of vertical acceleration. For example, the explanatory variables include feature quantities extracted from the mid-swing phase of gait waveform data of forward acceleration. For example, the explanatory variables include feature quantities extracted from the early swing phase of gait waveform data of angles in the horizontal plane. For example, the explanatory variables include feature quantities related to the foot angle in the swing phase. The data acquisition unit acquires feature quantity data including feature quantities extracted in response to the user's gait. For example, the data acquisition unit acquires feature quantities from the load response phase of gait waveform data of angular velocity in the coronal plane. For example, the data acquisition unit acquires feature quantities from the early swing phase of gait waveform data of vertical acceleration. For example, the data acquisition unit acquires a feature value of the mid-swing phase of gait waveform data of forward acceleration. For example, the data acquisition unit acquires a feature value of the early swing phase of gait waveform data of angles in a horizontal plane. For example, the data acquisition unit acquires a feature value of the foot angle during the swing phase. The estimation unit inputs the acquired feature value data into an estimation model. The estimation unit estimates the user's dynamic balance based on the user's dynamic balance index output from the estimation model. According to this aspect, by using an estimation model that has learned feature values ​​extracted from gait waveform data that includes features corresponding to muscle activity that affects dynamic balance, it is possible to estimate dynamic balance that is more suited to physical activity using sensor data related to foot movement.

[0092] In one aspect of the present embodiment, the dynamic balance estimation device is implemented in a terminal device having a screen viewable by a user. For example, the dynamic balance estimation device displays information related to dynamic balance estimated in accordance with the user's foot movement on the screen of the terminal device. For example, the dynamic balance estimation device displays recommendation information corresponding to the dynamic balance estimated in accordance with the user's foot movement on the screen of the terminal device. For example, the dynamic balance estimation device displays a video related to training for strengthening body parts related to dynamic balance on the screen of the terminal device as recommendation information corresponding to the dynamic balance estimated in accordance with the user's foot movement. According to this aspect, by displaying the dynamic balance estimated in accordance with features extracted from sensor data related to the user's foot movement on a screen viewable by the user, the user can check information corresponding to their own dynamic balance.

[0093] (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 dynamic balance in response to input of feature amounts by learning using feature amount data extracted from sensor data measured by a gait measurement device.

[0094] (composition) FIG. 26 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. 26 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.

[0095] Gait measurement device 20 is installed on at least one of the left and right feet. Gait measurement device 20 has the same configuration as gait measurement device 10 of the first embodiment. Gait measurement device 20 includes an acceleration sensor and an angular velocity sensor. Gait measurement device 20 converts measured physical quantities into digital data (also referred to as sensor data). Gait measurement device 20 generates gait waveform data for a normalized stride cycle from the time-series data of the sensor data. Gait measurement device 20 generates feature data used for estimating dynamic balance. Gait measurement device 20 transmits the generated feature data to learning device 25. Gait measurement device 20 may be configured to transmit the feature data to a database (not shown) accessed by learning device 25. The feature data stored in the database is used for learning by learning device 25.

[0096] The learning device 25 receives feature data from the gait measurement device 20. When feature data stored in a database (not shown) is used, the learning device 25 receives the feature data from the database. The learning device 25 performs learning using the received feature data. For example, the learning device 25 learns training data in which feature data extracted from gait waveform data of multiple subjects is used as an explanatory variable and a value related to dynamic balance corresponding to the feature data is used as a target variable. There are no particular limitations on the learning algorithm performed by the learning device 25. The learning device 25 generates an estimation model trained using training data related to multiple subjects. The learning device 25 stores the generated estimation model. The estimation model trained by the learning device 25 may be stored in a storage device external to the learning device 25.

[0097] [Learning device] Next, details of the learning device 25 will be described with reference to the drawings. Fig. 27 is a block diagram showing an example of the detailed configuration of the learning device 25. The learning device 25 has a receiving unit 251, a learning unit 253, and a storage unit 255.

[0098] Receiving unit 251 receives feature amount data from gait measurement device 20. Receiving unit 251 outputs the received feature amount data to learning unit 253. Receiving unit 251 may receive feature amount data from gait measurement device 20 via a wired connection such as a cable, or may receive feature amount data from gait measurement device 20 via wireless communication. For example, receiving unit 251 is configured to receive feature amount data from gait measurement device 20 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 receiving unit 251 may be compliant with standards other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0099] The learning unit 253 acquires 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 feature amount data extracted from sensor data measured according to the foot movement of the subject as an explanatory variable and learns a data set using teacher data in which the subject's FR distance is a target variable. For example, the learning unit 253 generates an estimation model that estimates the FR distance according to input feature amount data, learned for multiple subjects. For example, the learning unit 253 generates an estimation model according to attribute data (height). For example, the learning unit 253 generates an estimation model that estimates the FR distance as dynamic balance, using feature amount data extracted from sensor data measured according to the foot movement of the subject and the subject's attribute data (height) as explanatory variables. The learning unit 253 stores the estimation models learned for multiple subjects in the storage unit 255.

[0100] For example, the learning unit 253 performs learning using a linear regression algorithm. For example, the learning unit 253 performs learning using a support vector machine (SVM) algorithm. For example, the learning unit 253 performs learning using a Gaussian process regression (GPR) algorithm. For example, the learning unit 253 performs learning using a random forest (RF) algorithm. For example, the learning unit 253 may perform unsupervised learning that classifies the subjects that generated feature amount data according to the feature amount data. There are no particular limitations on the learning algorithm performed by the learning unit 253.

[0101] The learning unit 253 may perform learning using walking waveform data for one step cycle as explanatory variables. For example, the learning unit 253 performs supervised learning using walking waveform data of acceleration in three axial directions, angular velocity around three axes, and angle around three axes (posture angle) as explanatory variables, and the correct value of the dynamic balance index as the objective variable. For example, if the walking phase is set in 1% increments in a walking cycle from 0 to 100%, the learning unit 253 performs learning using 909 explanatory variables.

[0102] Fig. 28 is a conceptual diagram illustrating learning for generating an estimation model. Fig. 28 is a conceptual diagram showing an example in which the learning unit 253 learns using, as training data, a data set of feature amounts F1 to F5, which are explanatory variables, and FR distance (dynamic balance index), which is a response variable. For example, the learning unit 253 learns data related to a plurality of subjects and generates an estimation model that outputs (estimates) related to the FR distance (dynamic balance index) in response to input of feature amounts extracted from sensor data.

[0103] The storage unit 255 stores estimation models trained on a plurality of subjects. For example, the storage unit 255 stores estimation models for estimating dynamic balance trained on a plurality of subjects. For example, the estimation models stored in the storage unit 255 are used for estimating dynamic balance by the dynamic balance estimation device 13 of the first embodiment.

[0104] As described above, the learning system of this embodiment includes a gait measurement device and a learning device. The gait measurement device acquires time-series sensor data related to foot movement. The gait measurement device extracts gait waveform data for one step cycle from the time-series sensor data and normalizes the extracted gait waveform data. The gait measurement device extracts, from the normalized gait waveform data, feature amounts used to estimate the user's dynamic balance from a walking phase cluster composed of at least one temporally consecutive walking phase. The gait measurement device generates feature amount data including the extracted feature amounts. The gait measurement device outputs the generated feature amount data to the learning device.

[0105] The learning device has a receiving unit, a learning unit, and a storage unit. The receiving unit acquires feature data generated by the gait measurement device. The learning unit performs learning using the feature data. The learning unit generates an estimation model that outputs dynamic balance in response to input of features (second feature values) of walking phase clusters extracted from time-series data of sensor data measured as the user walks. The estimation model generated by the learning unit is stored in the storage unit.

[0106] The learning system of this embodiment generates an estimation model using feature data measured by a gait measurement device, and therefore, according to this aspect, it is possible to generate an estimation model that allows dynamic balance to be appropriately estimated in daily life without using any equipment for measuring dynamic balance.

[0107] (Third embodiment) Next, a dynamic balance estimation device according to a third embodiment will be described with reference to the drawings. The dynamic balance estimation device of this embodiment has a simplified configuration of the dynamic balance estimation device included in the dynamic balance estimation system of the first embodiment.

[0108] 29 is a block diagram showing an example of the configuration of the dynamic balance estimation device 33 according to this embodiment. The dynamic balance estimation device 33 includes a data acquisition unit 331, a storage unit 332, an estimation unit 333, and an output unit 335.

[0109] The data acquisition unit 331 acquires feature data extracted from sensor data related to the user's foot movement, including feature amounts used to estimate the user's dynamic balance index. The storage unit 332 stores an estimation model that outputs a dynamic balance index according to input feature data. The estimation unit 333 inputs the acquired feature data into the estimation model and estimates the user's dynamic balance according to the dynamic balance index output from the estimation model. The output unit 335 outputs information related to the estimated dynamic balance.

[0110] As described above, in this embodiment, the dynamic balance of a user is estimated using feature amounts extracted from sensor data related to the movement of the user's feet. Therefore, according to this embodiment, the dynamic balance can be appropriately estimated in daily life without using any equipment for measuring dynamic balance.

[0111] (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. 30 as an example. Note that the information processing device 90 in Fig. 30 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0112] As shown in Fig. 30, an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 30, interface is abbreviated as I / F (Interface). The processor 91, the main storage 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 able to communicate data with each other. The processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are also connected to a network such as the Internet or an intranet via the communication interface 96.

[0113] The processor 91 loads a program stored in an auxiliary storage device 93 or the like into a main storage device 92. The processor 91 executes the program loaded into the main storage device 92. In this embodiment, a software program installed in the information processing device 90 may be used. The processor 91 executes control and processing according to each embodiment.

[0114] The main memory device 92 has an area in which programs are loaded. Programs stored in the auxiliary memory device 93 or the like are loaded into the main memory device 92 by the processor 91. The main memory device 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). Furthermore, a non-volatile memory such as an MRAM (Magnetoresistive Random Access Memory) may be configured / added to the main memory device 92.

[0115] 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 flash memory. Note that it is also possible to configure the main storage device 92 to store various data, thereby omitting the auxiliary storage device 93.

[0116] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0117] Input devices such as a keyboard, mouse, and touch panel may be connected to the information processing device 90 as needed. These input devices are used to input information and settings. When a touch panel is used as the input device, the display screen of the display device may also serve as the interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0118] The information processing device 90 may also be equipped with a display device for displaying information. When a display device is equipped, the information processing device 90 preferably includes 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.

[0119] The information processing device 90 may also be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) for reading data and programs from the recording medium, writing the processing results of the information processing device 90 to the recording medium, etc. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0120] The above is an example of a hardware configuration for enabling control and processing according to each embodiment of the present invention. Note that the hardware configuration in FIG. 30 is an example of a hardware configuration for executing control and processing according to each embodiment, and does not limit the scope of the present invention. Furthermore, a program that causes a computer to execute control and processing according to each embodiment is also within the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also within the scope of the present invention. The recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be realized as a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0121] The components of each embodiment may be combined in any manner, and may be realized by software or by a circuit.

[0122] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications 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.

[0123] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) a data acquisition unit that acquires feature data including feature amounts used to estimate the dynamic balance of the user, the feature amounts being extracted from sensor data related to the user's foot movements; a storage unit that stores an estimation model that outputs a dynamic balance index in response to input of the feature amount data; an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the dynamic balance of the user according to the dynamic balance index output from the estimation model; an output unit that outputs information related to the estimated dynamic balance of the user. (Appendix 2) The data acquisition unit A dynamic balance estimation device as described in Appendix 1, which acquires feature data including features used to estimate a performance value of a functional reach test as the dynamic balance index, extracted from gait waveform data generated using time series data of the sensor data regarding foot movement. (Appendix 3) The storage unit storing the estimation model generated by learning using training data for a plurality of subjects, the training data having feature quantities used to estimate the dynamic balance index as explanatory variables and the dynamic balance indexes of the plurality of subjects as objective variables; The estimation unit 3. The dynamic balance estimation device according to claim 2, wherein the feature data acquired about the user is input to the estimation model, and the dynamic balance of the user is estimated according to the dynamic balance index of the user output from the estimation model. (Appendix 4) The storage unit storing the estimation model trained using explanatory variables including the heights of the plurality of subjects; The estimation unit 4. The dynamic balance estimation device according to claim 3, wherein the feature data and height of the user are input to the estimation model, and the dynamic balance of the user is estimated based on the dynamic balance index of the user output from the estimation model. (Appendix 5) The storage unit 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 load response phase, a feature amount related to the activity of the iliacus muscle extracted from the early swing phase, a feature amount related to the tibialis anterior muscle and the short head of the biceps femoris muscle extracted from the mid-swing phase, and a feature amount related to the compensatory movement of the foot angle during the swing phase are used as explanatory variables, and the dynamic balance index of the plurality of subjects is used as a response variable. The estimation model is stored; The estimation unit 5. The dynamic 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 dynamic balance of the user is estimated in accordance with the dynamic 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 quantity of angular velocity in the coronal plane extracted from a load response period of the gait waveform data, a feature quantity of vertical acceleration extracted from an early swing phase of the gait waveform data, a feature quantity of forward acceleration extracted from a mid-swing phase of the gait waveform data, a feature quantity of angle in the horizontal plane extracted from an early swing phase of the gait waveform data, and a feature quantity related to the foot angle in the swing phase, and the dynamic 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, the feature amount including a feature amount of the angular velocity in the coronal plane in a load response period of the walking waveform data, a feature amount of the vertical acceleration in the walking waveform data in an early swing phase, a feature amount of the forward acceleration in the walking waveform data in a mid-swing phase, a feature amount of the angle in the horizontal plane in an early swing phase of the walking waveform data, and a feature amount of the foot angle in the swing phase; The estimation unit 6. A dynamic balance estimation device according to claim 5, wherein the acquired feature data is input to the estimation model, and the dynamic balance of the user is estimated according to the dynamic balance index of the user output from the estimation model. (Appendix 7) The estimation unit estimating information about the dynamic balance of the user in response to the estimated dynamic balance index for the user; The output unit 7. A dynamic balance estimation device according to claim 3, which outputs information relating to the estimated dynamic balance. (Appendix 8) A dynamic balance estimation device according to any one of Supplementary Notes 1 to 7; 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 dynamic balance from a gait phase cluster formed by at least one gait phase that is consecutive in time, generates feature data including the extracted feature data, and outputs the generated feature data to the dynamic balance estimation device. (Appendix 9) The dynamic balance estimation device includes: implemented in a terminal device having a screen viewable by the user, 9. The dynamic balance estimation system according to claim 8, wherein information about the dynamic balance estimated according to the movement of the user's feet is displayed on a screen of the terminal device. (Appendix 10) The dynamic balance estimation device includes: 10. The dynamic balance estimation system according to claim 9, wherein recommendation information according to the dynamic balance estimated according to the foot movement of the user is displayed on a screen of the terminal device. (Appendix 11) The dynamic balance estimation device includes: The dynamic balance estimation system according to claim 10, wherein a video relating to training for strengthening a body part related to the dynamic balance is displayed on a screen of the terminal device as the recommendation information according to the dynamic balance estimated according to the movement of the user's feet. (Appendix 12) The computer acquiring feature data including feature values ​​used to estimate the dynamic balance of the user, the feature values ​​being extracted from sensor data relating to the user's foot movements; inputting the acquired feature amount data into an estimation model that outputs a dynamic balance index in response to the input of the feature amount data; estimating the dynamic balance of the user according to the dynamic balance index output from the estimation model; A dynamic balance estimation method that outputs information about the estimated dynamic balance of the user. (Appendix 13) acquiring feature data including feature values ​​used to estimate the dynamic balance of the user, the feature values ​​being extracted from sensor data relating to the user's foot movements; a process of inputting the acquired feature amount data into an estimation model that outputs a dynamic balance index according to the input of the feature amount data; a process of estimating the dynamic balance of the user according to the dynamic balance index output from the estimation model; and outputting information relating to the estimated dynamic balance of the user. [Explanation of symbols]

[0124] 1 Dynamic balance estimation system 2. Learning System 10, 20 Gait measurement device 11 Sensors 12 Feature data generation unit 13 Dynamic balance estimation device 25 Learning Device 111 Acceleration Sensor 112 Angular rate sensor 121 Acquisition Department 122 Normalization section 123 Extraction part 125 Generation part 127 Feature data output unit 131, 331 Data acquisition section 132, 332 storage section 133, 333 Estimation part 135, 335 output section 251 Receiving unit 253 Learning Department 255 Storage section

Claims

1. a data acquisition means for acquiring feature data including feature values ​​used to estimate a performance score of a functional reach test as a dynamic balance index of the user, the feature values ​​being extracted from gait waveform data generated using time-series data of sensor data related to the user's foot movements; a storage means for storing an estimation model that uses, as explanatory variables, feature values ​​related to the activity of the gluteus medius muscle extracted from the load response period, feature values ​​related to the activity of the iliacus muscle extracted from the early swing phase, feature values ​​related to the tibialis anterior muscle and the short head of the biceps femoris muscle extracted from the mid-swing phase, and feature values ​​related to the compensatory movement of the foot angle during the swing phase, with respect to the gait waveform data of a plurality of subjects, the estimation model being generated by learning using training data that uses the dynamic balance indexes of the plurality of subjects as objective variables, and that outputs the dynamic balance indexes in response to input of the feature value data; an estimation means for inputting the feature data acquired in response to the user's walking into the estimation model and estimating the dynamic balance of the user in response to the dynamic balance index of the user output from the estimation model; and an output unit that outputs information relating to the estimated dynamic balance of the user.

2. The storage means storing the estimation model trained using explanatory variables including the heights of the plurality of subjects; The estimation means 2. The dynamic balance estimation device according to claim 1, wherein the feature data and height of the user are input to the estimation model, and the dynamic balance of the user is estimated based on the dynamic balance index of the user output from the estimation model.

3. The storage means the estimation model is generated by learning using training data in which, for a plurality of the subjects, explanatory variables are a feature quantity of angular velocity in the coronal plane extracted from a load response period of the gait waveform data, a feature quantity of vertical acceleration extracted from an early swing phase of the gait waveform data, a feature quantity of forward acceleration extracted from a mid-swing phase of the gait waveform data, a feature quantity of angle in the horizontal plane extracted from an early swing phase of the gait waveform data, and a feature quantity related to the foot angle in the swing phase, and the dynamic balance index of the plurality of the subjects is used as a response variable; and The data acquisition means Acquire the feature amount data extracted in response to the user's walking, including a feature amount of the angular velocity in the coronal plane in the load response period of the walking waveform data, a feature amount of the vertical acceleration in the walking waveform data in the early swing phase, a feature amount of the forward acceleration in the walking waveform data in the mid swing phase, a feature amount of the angle in the horizontal plane in the early swing phase of the walking waveform data, and a feature amount of the foot angle in the swing phase; The estimation means 3. The dynamic balance estimation device according to claim 1, wherein the acquired feature data is input to the estimation model, and the dynamic balance of the user is estimated based on the dynamic balance index of the user output from the estimation model.

4. The dynamic balance estimation device according to any one of claims 1 to 3; a gait measurement device having: a sensor that is attached to footwear of a user who is a target of dynamic balance estimation, measures spatial acceleration and spatial angular velocity, generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data; and a feature data generation means that acquires time-series data of the sensor data including gait features, extracts gait waveform data for one step cycle from the time-series data of the sensor data, normalizes the extracted gait waveform data, extracts feature quantities to be used for estimating the dynamic balance from a gait phase cluster formed by at least one temporally consecutive gait phase, generates feature data including the extracted feature quantities, and outputs the generated feature data to the dynamic balance estimation device.

5. The dynamic balance estimation device includes: implemented in a terminal device having a screen viewable by the user, The dynamic balance estimation system according to claim 4 , wherein the information about the dynamic balance estimated in accordance with the movement of the user's feet is displayed on a screen of the terminal device.

6. The dynamic balance estimation device includes: The dynamic balance estimation system according to claim 5 , wherein recommendation information corresponding to the dynamic balance estimated according to the foot movements of the user is displayed on a screen of the terminal device.

7. The computer acquiring feature data including feature values ​​used to estimate a performance value of a functional reach test as a dynamic balance index of the user, the feature value being extracted from gait waveform data generated using time-series data of sensor data relating to the user's foot movement; inputting the feature data acquired in response to the user's walking into an estimation model that uses, as explanatory variables, feature data relating to the activity of the gluteus medius extracted from the load response phase, feature data relating to the activity of the iliacus extracted from the early swing phase, feature data relating to the tibialis anterior and the short head of the biceps femoris extracted from the mid-swing phase, and feature data relating to the compensatory movement of the foot angle during the swing phase, with respect to the walking waveform data of a plurality of subjects, the estimation model being generated by learning using training data that uses the dynamic balance indexes of the plurality of subjects as objective variables, and that outputs the dynamic balance indexes in response to input of the feature data; estimating the dynamic balance of the user according to the dynamic balance index of the user output from the estimation model; A dynamic balance estimation method that outputs information about the estimated dynamic balance of the user.

8. a process of acquiring feature data including features used to estimate a performance score of a functional reach test as a dynamic balance index of the user, the feature data being extracted from gait waveform data generated using time-series data of sensor data related to the user's foot movements; a process of inputting the feature amount data acquired in response to the user's walking into an estimation model that outputs the dynamic balance index in response to input of the feature amount data, the estimation model being generated by learning using training data that uses, as explanatory variables, feature amounts related to the activity of the gluteus medius muscle extracted from the load response period, feature amounts related to the activity of the iliacus muscle extracted from the early swing phase, feature amounts related to the tibialis anterior muscle and the short head of the biceps femoris muscle extracted from the mid-swing phase, and feature amounts related to the compensatory movement of the foot angle during the swing phase, and the dynamic balance index of the multiple subjects as a response variable, with respect to the walking waveform data of the multiple subjects; a process of estimating a dynamic balance of the user according to the dynamic balance index of the user output from the estimation model; and outputting information relating to the estimated dynamic balance of the user.

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