LOWER LIMB MUSCLE STRENGTH ESTIMATION DEVICE, LOWER LIMB MUSCLE STRENGTH ESTIMATION SYSTEM, LOWER LIMB MUSCLE STRENGTH ESTIMATION METHOD, AND PROGRAM

Abstract

In order to appropriately estimate lower limb muscle power in daily life, this lower limb muscle power estimation device is equipped with: a data acquisition unit that acquires feature amount data including a feature amount which is extracted from sensor data pertaining to the motion of a leg of a user and which is used to estimate lower limb muscle power of the user; a storage unit that stores an estimation model for outputting a lower limb muscle power index according to input of the feature amount data; an estimation unit that inputs the acquired feature amount data to the estimation model and estimates the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model; and an output unit that outputs information on the estimated lower limb muscle power of the user.

Description

[Technical field]

[0001] The present disclosure relates to a lower limb muscle strength estimation device and the like that estimates lower limb muscle strength using sensor data related to foot movement. [Background technology]

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

[0003] Patent Document 1 discloses an information processing device that extracts features used for personal identification from foot motion information. The device of Patent Document 1 acquires foot motion information measured by a motion measuring device attached to the user's foot. The device of Patent Document 1 extracts features used for user identification from time-series data of one walking cycle included in the motion information.

[0004] Patent Document 2 discloses a method for suggesting exercises to improve motor function according to the walking ability of a subject. In the method of Patent Document 2, the walking ability of a subject is obtained by having the subject walk a predetermined distance. In the method of Patent Document 2, the risk of falling is determined from the value of walking ability. In the method of Patent Document 2, an exercise menu for preventing falls and improving motor function is suggested according to walking ability and fall risk. In the method of Patent Document 2, the suggested exercise menu is displayed, and walking ability and fall risk are also displayed.

[0005] Sit-to-stand movements are important movements in daily life. Sit-to-stand movements are related to lower limb muscle strength. Lower limb muscle strength is an important indicator for evaluating frailty and fall risk.

[0006] Non-Patent Document 1 reports an example of verification of muscles related to the results of the 30-second chair stand (CS-30) test, which measures the number of times a person stands up and sits down on a chair in 30 seconds (CS: Chair-Stand). Non-Patent Document 1 reports that the results of the CS-30 test are correlated with the strength of the iliopsoas, gluteus maximus, hamstrings, and quadriceps.

[0007] Non-Patent Document 2 reports an example of verification of muscles related to the results of the CS-30 test conducted on young women. Non-Patent Document 2 reports results suggesting that the results of the CS-30 test are related to the muscle strength of the gluteus maximus, which is the hip joint extensor muscle, and the hamstrings, which are the knee joint flexor muscles, and the posterior muscles of the lower limbs.

[0008] Non-Patent Document 3 reports on the sequence of muscle contraction of the lower limb muscles during the standing up motion. Non-Patent Document 3 reports that the sequence of muscle contraction of the tibialis anterior, gastrocnemius, quadriceps, and biceps femoris during the free standing up motion of a healthy adult from a chair can be summarized into roughly three patterns. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] International Publication No. 2020 / 240751 [Patent Document 2] JP 2008-229266 A [Non-patent literature]

[0010] [Non-Patent Document 1] Yusuke Chigira, “Lower limb muscles that affect standing and sitting ability,” 2nd Japan Training Instructors Association Conference, Poster Presentation 01, 2013. [Non-Patent Document 2] Chiaki Yagura et al., “Relationship between the 30-second chair stand test and lower limb muscle function in young women”, Physical Therapy Supplement, Vol.30 Suppl. No.2, (Abstracts of the 38th Japanese Physical Therapy Congress), pp.143, 2003. [Non-Patent Document 3] Koji Kijima et al., “Sequence of muscle contraction of lower limb muscles during standing-to-stand movement”, Physical Therapy Supplement Vol.31 Suppl. No.2, (Abstracts of the 39th Annual Meeting of the Japanese Physical Therapy Society), pp. A0245, 2004. Summary of the Invention [Problem to be solved by the invention]

[0011] The method of Patent Document 1 performs personal identification using features extracted from foot motion information measured by a motion measuring device attached to the user's feet. Patent Document 1 does not disclose estimating lower limb muscle strength using gait features of characteristic parts extracted from data acquired from a sensor attached to footwear.

[0012] In the method of Patent Document 2, an estimated index related to the walking ability of a subject is calculated according to the acceleration measured by an accelerometer attached to the subject's waist. In the method of Patent Document 2, walking ability such as walking speed, stride length, knee extension force, and dorsiflexion force is estimated according to the calculated estimated index. The method of Patent Document 2 estimates walking ability according to waist movement, but cannot verify lower limb muscle strength according to foot movement.

[0013] Lower limb muscle strength can be evaluated if standing and sitting on a chair 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 lower limb muscle strength in daily life.

[0014] An object of the present disclosure is to provide a lower limb muscle strength estimation device etc. that can appropriately estimate lower limb muscle strength in daily life. [Means for solving the problem]

[0015] A lower limb muscle strength estimation device of one embodiment of the present disclosure includes a data acquisition unit that acquires feature data including features used to estimate a user's lower limb muscle strength extracted from sensor data related to the movement of the user's legs, a memory unit that stores an estimation model that outputs a lower limb muscle strength index according to an input of the feature data, an estimation unit that inputs the acquired feature data into the estimation model and estimates the user's lower limb muscle strength according to the lower limb muscle strength index output from the estimation model, and an output unit that outputs information related to the estimated lower limb muscle strength of the user.

[0016] In one embodiment of the lower limb muscle strength estimation method of the present disclosure, feature data including features used to estimate the user's lower limb muscle strength extracted from sensor data related to the user's foot movement is acquired, the acquired feature data is input into an estimation model that outputs a lower limb muscle strength index according to the input of the feature data, the user's lower limb muscle strength is estimated according to the lower limb muscle strength index output from the estimation model, and information related to the estimated lower limb muscle strength of the user is output.

[0017] A program of one embodiment of the present disclosure causes a computer to perform the following processes: acquiring feature data including features used to estimate a user's lower limb muscle strength, extracted from sensor data related to the movement of the user's feet; inputting the acquired feature data into an estimation model that outputs a lower limb muscle strength index according to the input of the feature data; estimating the user's lower limb muscle strength according to the lower limb muscle strength index output from the estimation model; and outputting information related to the estimated lower limb muscle strength of the user. Effect of the Invention

[0018] According to the present disclosure, it is possible to provide a lower limb muscle strength estimation device etc. that can appropriately estimate lower limb muscle strength in daily life. [Brief description of the drawings]

[0019] [Figure 1] 1 is a block diagram showing an example of the configuration of a lower limb muscle strength estimation system according to a first embodiment. [Diagram 2]1 is a block diagram showing an example of the configuration of a gait measurement device included in a lower limb muscle strength estimation system according to a first embodiment. FIG. [Diagram 3] 1 is a conceptual diagram showing an example of arrangement of a gait measurement device according to a first embodiment. FIG. [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. [Diagram 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] 4 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 8] 4 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. FIG. [Figure 9] 3 is a conceptual diagram for explaining an example of a walking phase cluster from which a feature amount data generating unit of the gait measurement device according to the first embodiment extracts feature amounts. FIG. [Figure 10] 1 is a block diagram showing an example of the configuration of a lower limb muscle strength estimation device included in a lower limb muscle strength estimation system according to a first embodiment. FIG. [Figure 11] FIG. 2 is a conceptual diagram for explaining a five-times chair standing test for evaluating lower limb muscle strength that is an estimation target of the lower limb muscle strength estimation system according to the first embodiment. [Figure 12] 1 is a table related to specific examples of feature amounts extracted for estimating five standing-up and sitting-down times by the gait measurement device provided in the lower limb muscle strength estimation system according to the first embodiment. [Figure 13] 13 is a graph showing the correlation between a feature amount F1 extracted by a gait measurement device included in the lower limb muscle strength estimation system according to the first embodiment and the actually measured five standing-up and sitting-down times. [Figure 14]13 is a graph showing the correlation between a feature amount F2 extracted by a gait measurement device included in the lower limb muscle strength estimation system according to the first embodiment and the actually measured five standing-up and sitting-down times. [Figure 15] 13 is a graph showing the correlation between a feature amount F3 extracted by the gait measurement device included in the lower limb muscle strength estimation system according to the first embodiment and the actually measured five standing-up and sitting-down times. [Figure 16] 13 is a graph showing the correlation between a feature amount F4 extracted by the gait measurement device included in the lower limb muscle strength estimation system according to the first embodiment and the actually measured five standing-up and sitting-down times. [Figure 17] FIG. 2 is a block diagram showing an example of estimation of five standing-up and sitting-down times (lower limb muscle strength index) by a lower limb muscle strength estimation device included in the lower limb muscle strength estimation system according to the first embodiment. [Figure 18] This is a graph showing the correlation between the estimated five-stand-sit-down time estimated using an estimation model generated by learning with gender, age, height, weight, and walking speed as explanatory variables and the measured five-stand-sit-down time. [Figure 19] 1 is a graph showing a correlation between an estimated value of the five standing-up and sitting-down time estimated by a lower limb muscle strength estimation device provided in the lower limb muscle strength estimation system according to the first embodiment and a measured value of the five standing-up and sitting-down time. [Figure 20] 5 is a flowchart for explaining an example of the operation of the gait measurement device included in the lower limb muscle strength estimation system according to the first embodiment. [Figure 21] 5 is a flowchart for explaining an example of an operation of a lower limb muscle strength estimation device included in a lower limb muscle strength estimation system according to the first embodiment. [Figure 22] FIG. 2 is a conceptual diagram for explaining an application example of the lower limb muscle strength estimation system according to the first embodiment. [Figure 23] FIG. 11 is a block diagram showing an example of the configuration of a learning system according to a second embodiment. [Figure 24] FIG. 11 is a block diagram showing an example of the configuration of a learning device included in a learning system according to a second embodiment. [Diagram 25]FIG. 11 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 26] FIG. 11 is a block diagram showing an example of the configuration of a lower limb muscle strength estimation device according to a third embodiment. [Figure 27] 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 PREFERRED EMBODIMENTS

[0020] Hereinafter, the 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 addition, in all the drawings used to explain the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. In addition, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0021] (First embodiment) First, a lower limb muscle strength estimation system according to a first embodiment will be described with reference to the drawings. The lower limb muscle strength estimation system of this embodiment measures sensor data related to foot movement according to a user's walking. The lower limb muscle strength estimation system of this embodiment estimates the lower limb muscle strength of the user using the measured sensor data.

[0022] In this embodiment, an example of estimating the results of a 5-sit-to-stand test in which the subject repeats standing up and sitting down on a chair 5 times will be given as the lower limb muscle strength. Hereinafter, the 5-sit-to-stand test will also be referred to as the SS-5 (Sit to Stand-5) test. In this embodiment, the results of the SS-5 test are evaluated based on the time it takes to repeatedly stand up and sit down on a chair 5 times (also referred to as the 5-sit-to-stand time). The 5-sit-to-stand time is the score value of the SS-5 test. The shorter the 5-sit-to-stand time, the higher the score of the SS-5 test. The method of this embodiment can also be applied to the results of sit-to-stand tests other than the SS-5 test. For example, the method of this embodiment can also be applied to the results of a 30-second chair-stand (CS-30) test in which the number of times the subject stands up and sits down on a chair in 30 seconds is measured (CS: Chair-Stand).

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

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

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

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

[0027] The angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. The angular velocity sensor 112 measures the 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 generating unit 12. For example, a vibration type, a capacitance type, or other type sensor can be used as the angular velocity sensor 112. There is no limitation on the measurement type of the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0028] 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.

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

[0030] In the example of FIG. 3, a local coordinate system is set with the gait measurement device 10 (sensor 11) as a 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 with the same specifications are placed in the 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 that case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right feet.

[0031] FIG. 4 is a conceptual diagram for explaining a local coordinate system (x-axis, y-axis, z-axis) set in the gait measurement device 10 (sensor 11) installed on the back side 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 stands upright facing the direction of travel, the user's lateral direction is set as the X-axis direction (leftward is positive), the direction of the user's back is set as the Y-axis direction (backward is positive), and the direction of gravity is set as the Z-axis direction (vertically upward is positive). 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 that changes according to the user's walking.

[0032] FIG. 5 is a conceptual diagram for explaining a plane (also called a human body plane) 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 stands upright with the center line of the foot facing the direction of travel, the world coordinate system and the local coordinate system coincide. In this embodiment, the rotation in the sagittal plane with the x-axis as the rotation axis is defined as roll, the rotation in the coronal plane with the y-axis as the rotation axis is defined as pitch, and the rotation in the horizontal plane with the z-axis as the rotation axis is defined as yaw. In addition, the rotation angle in the sagittal plane with the x-axis as the rotation axis is defined as roll angle, the rotation angle in the coronal plane with the y-axis as the rotation axis is defined as pitch angle, and the rotation angle in the horizontal plane with the z-axis as the rotation axis is defined as yaw angle.

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

[0034] The acquiring unit 121 acquires the acceleration in three axial directions from the acceleration sensor 111. The acquiring unit 121 also acquires the angular velocity around three axes from the angular velocity sensor 112. For example, the acquiring unit 121 performs AD conversion (Analog-to-Digital Conversion) of the acquired physical quantities (analog data) such as the angular velocity and acceleration. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. The acquiring unit 121 outputs the converted digital data (also called 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 the acceleration data converted into digital data and the angular velocity data converted into digital data. The acceleration data includes acceleration vectors in 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 acquisition time of the data. Furthermore, the acquisition unit 121 may perform corrections such as correction for mounting error, temperature correction, and linearity correction on the acceleration data and angular velocity data.

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

[0036] FIG. 6 is a conceptual diagram for explaining a step cycle based on the right foot. A step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 6 indicates one walking cycle of the right foot, starting from the time when the heel of the right foot lands on the ground and ending from the time when the heel of the right foot lands on the ground. The horizontal axis of FIG. 6 is first normalized so that the step cycle is 100%. The horizontal axis of FIG. 6 is second normalized so that the stance phase is 60% and the swing phase is 40%. One walking cycle of one foot is roughly divided into a stance phase in which at least a part 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 phase T2, a final stance phase T3, and an early swing phase T4. The swing phase is further divided into an early swing phase T5, a mid-swing phase T6, and a final swing phase T7. Note that FIG. 6 is merely an example and does not limit the periods that constitute a gait cycle or the names of these periods.

[0037] As shown in Figure 6, multiple events (also called walking events) occur during walking. E1 represents an event in which the heel of the right foot touches the ground (heel contact) (HC). E2 represents an event in which the toe of the left foot leaves the ground (opposite toe off) (OTO). E3 represents an event in which the heel of the right foot rises (heel rise) (HR) while the sole of the right foot touches the ground. E4 represents an event in which the heel of the left foot touches the ground (opposite heel strike) (OHS). E5 represents an event in which the toe of the right foot leaves the ground (toe off) (TO) while the sole of the left foot touches the ground. E6 represents an event in which the left and right feet cross (foot crossing) while the sole of the left foot touches the ground (foot adjacent) (FA). E7 represents an event where the tibia of the right foot is almost perpendicular to the ground (Tibia Vertical: TV) with the sole of the left foot touching the ground. E8 represents an 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 starts from E1 and corresponds to the starting point of the next walking cycle. Note that FIG. 6 is an example and does not limit the events that occur during walking or the names of those events.

[0038] FIG. 7 is a diagram for explaining an example of detecting heel strike HC and toe lift TO from time series data (solid line) of travel direction acceleration (Y direction acceleration). The timing of heel strike HC is the timing of a minimum peak immediately after a maximum peak that appears in the time series data of travel direction acceleration (Y direction acceleration). The maximum peak that marks the timing of heel strike HC corresponds to the maximum peak of the walking waveform data for one step cycle. The section between successive 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 in which no fluctuation appears in the time series data of travel direction acceleration (Y direction acceleration). FIG. 7 also shows time series data (dashed line) of roll angle (angular velocity around X axis). The timing of 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 based on the mid-stance phase.

[0039] FIG. 8 is a diagram for explaining an example of gait waveform data normalized by the normalization unit 122. The normalization unit 122 detects heel strike HC and toe lift TO from time series data of travel direction acceleration (Y direction acceleration). The normalization unit 122 extracts the section between successive heel strikes HC as gait waveform data for one step gait cycle. The normalization unit 122 converts the horizontal axis (time axis) of the gait waveform data for one step gait cycle into a gait cycle of 0 to 100% by the first normalization. In FIG. 8, the gait waveform data after the first normalization is shown by a dashed line. In the gait waveform data after the first normalization (dashed line), the timing of toe lift TO is shifted from 60%.

[0040] In the example of FIG. 8, the normalization unit 122 normalizes the section from heel-contact HC, where the walking phase is 0%, to toe-off TO, which follows the heel-contact HC, to 0-60%. The normalization unit 122 also normalizes the section from toe-off TO to heel-contact 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. 8, the gait waveform data after the second normalization is shown by a solid line. In the gait waveform data after the second normalization (solid line), the timing of toe-off TO coincides with 60%.

[0041] 7 and 8 show an example in which gait waveform data for one step walking cycle is extracted / normalized based on the traveling direction acceleration (Y direction acceleration). With respect to acceleration / angular velocity other than the traveling direction acceleration (Y direction acceleration), the normalization unit 122 extracts / normalizes gait waveform data for one step walking cycle in accordance with the walking cycle of the traveling direction acceleration (Y direction acceleration). The normalization unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocity around three axes. In this case, the normalization unit 122 extracts / normalizes gait waveform data for one step walking cycle in accordance with the walking cycle of the traveling direction acceleration (Y direction acceleration) for angles around three axes as well.

[0042] 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 the 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 the vertical acceleration (Z-direction acceleration) becomes almost 0. 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 successive heel strikes HC is one step gait cycle. The timing of toe-off TO is the timing of an inflection point in the middle of the time series data of vertical acceleration (Z-direction acceleration) gradually increasing after a section of small fluctuation following a maximum peak immediately after heel-contact HC. The normalization unit 122 may also extract / normalize the gait waveform data for one step 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 step cycle based on acceleration, angular velocity, angle, etc. other than the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration).

[0043] The extraction unit 123 acquires walking waveform data for one step cycle normalized by the normalization unit 122. The extraction unit 123 extracts feature values ​​used for estimating lower limb muscle strength from the walking waveform data for one step cycle. The extraction unit 123 extracts feature values ​​for each walking phase cluster from a walking phase cluster that integrates temporally consecutive walking phases based on a preset condition. A walking phase cluster includes at least one walking phase. A walking phase cluster also includes a single walking phase. The walking waveform data and walking phases from which feature values ​​used for estimating lower limb muscle strength are extracted will be described later.

[0044] FIG. 9 is a conceptual diagram for explaining extraction of a feature amount for estimating a lower limb muscle strength from walking waveform data for a step cycle. For example, the extraction unit 123 extracts walking phases i to i+m that are continuous in time 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) that constitute the walking phase cluster C (also referred to as the number of components) is m. Although FIG. 9 shows an example in which the walking phases are integer values, the walking phases may be subdivided to the decimal point. When the walking phases are subdivided to the 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 a feature amount 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 a feature amount from the single walking phase j (j is a natural number).

[0045] The generating unit 125 applies the feature amount constituting formula to the feature amount (first feature amount) extracted from each of the walking phases constituting the walking phase cluster to generate a feature amount (second feature amount) of the walking phase cluster. The feature amount constituting formula is a calculation formula set in advance to generate a feature amount of the walking phase cluster. For example, the feature amount constituting formula is a calculation formula related to the four arithmetic operations. For example, the second feature amount calculated using the feature amount constituting formula is an integral average value, an arithmetic average value, a slope, a variation, or the like, of the first feature amount in each walking phase included in the walking phase cluster. For example, the generating unit 125 applies a calculation formula for calculating the slope or variation of the first feature amount extracted from each of the walking phases constituting the walking phase cluster as the feature amount constituting formula. For example, when the walking phase cluster is composed of a single walking phase, it is not possible to calculate the slope or variation, so it is sufficient to use a feature amount constituting formula for calculating an integral average value, an arithmetic average value, or the like.

[0046] 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 lower limb muscle strength estimation device 13 that uses the feature data.

[0047] [Lower limb muscle strength estimation device] 10 is a block diagram showing an example of the configuration of the lower limb muscle strength estimation device 13. The lower limb muscle strength estimation device 13 has a data acquisition unit 131, a storage unit 132, an estimation unit 133, and an output unit 135.

[0048] 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) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of data acquisition unit 131 may be conforming to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0049] The storage unit 132 stores an estimation model that estimates the five-time stand-up and sitting time as a lower limb muscle strength index using feature amount data extracted from the walking waveform data. The storage unit 132 stores an estimation model that learns the relationship between feature amount data related to the five-time stand-up and sitting time of multiple subjects and the five-time stand-up and sitting time. For example, the storage unit 132 stores an estimation model that estimates the five-time stand-up and sitting time that is learned for multiple subjects. The five-time stand-up and sitting time is affected by age. Therefore, the storage unit 132 may store an estimation model according to attribute data related to age.

[0050] Fig. 11 is a conceptual diagram for explaining the five-times sit-stand test (SS-5 test). The subject repeats the action of standing up from a seated position in a chair five times. Measurement starts from the seated position in the chair, and the subject repeats standing up and sitting down, and ends when the subject has stood up five times.

[0051] Lower limb muscle strength can be evaluated according to the time it takes to stand up and sit down five times. For people aged 60-69, the average time is about 11-12 seconds. For people aged 70-89, the average time is about 12-13 seconds. If it takes about 14-15 seconds to stand up and sit down five times, the risk of falling is high. The evaluation criteria for lower limb muscle strength according to the time it takes to stand up and sit down five times given here are only guidelines and may be set according to the situation.

[0052] The estimation model may be stored in the storage unit 132 at the time of shipment of the product from the factory, or at the time of calibration before the user uses the lower limb muscle strength estimation system 1. For example, the estimation model may be configured to be used that is stored in a storage device such as an external server. In this case, the estimation model may be configured to be used via an interface (not shown) connected to the storage device.

[0053] 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 execute estimation of the five standing and sitting times as the lower limb muscle strength. 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 lower limb muscle strength (five standing and sitting times) output from the estimation model. When using an estimation model stored in an external storage device constructed in a 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.

[0054] The output unit 135 outputs the estimation result of the lower limb muscle strength by the estimation unit 133. For example, the output unit 135 displays the estimation result of the lower limb muscle strength 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 the use of the lower limb muscle strength output from the lower limb muscle strength estimation device 13.

[0055] For example, the lower limb muscle strength estimation device 13 is connected to an external system constructed in a cloud or a server through a mobile terminal (not shown) carried by the subject (user). The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a mobile communication device having a communication function such as a smartphone, a smart watch, or a mobile phone. For example, the lower limb muscle strength estimation device 13 is connected to the mobile terminal through a wire such as a cable. For example, the lower limb muscle strength estimation device 13 is connected to the mobile terminal through wireless communication. For example, the lower limb muscle strength estimation device 13 is connected to the mobile terminal through a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the lower limb muscle strength estimation device 13 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark). The estimation result of the lower limb muscle strength may be used by an application installed in the mobile terminal. In that case, the mobile terminal executes a process using the estimation result by application software or the like installed in the mobile terminal.

[0056] [Estimated time spent standing and sitting five times] Next, the correlation between the five-times standing-up and sitting-down time and the feature data will be described with a verification example. FIG. 12 is a correspondence table summarizing the feature values ​​used to estimate the five-times standing-up and sitting-down time. The correspondence table in FIG. 12 associates the feature value number, the walking waveform data from which the feature value is extracted, the walking phase (%) from which the walking phase cluster is extracted, and the related muscles. The five-times standing-up and sitting-down time is correlated with the quadriceps, hamstrings, tibialis anterior, and gastrocnemius. Therefore, the feature values ​​F1 to F4 extracted from the walking phase in which these features appear are used to estimate the five-times standing-up and sitting-down time.

[0057] 13 to 16 show the results of verifying the correlation between the five standing-up and sitting-down times and feature data. Figs. 13 to 16 show the results of verifying the correlation between a total of 62 subjects, consisting of 27 men and 35 women aged 60 to 85 years old. Figs. 13 to 16 show the results of verifying the correlation between an estimated value estimated using feature values ​​extracted according to walking while wearing footwear equipped with the gait measurement device 10 and a measured value (true value) of the five standing-up and sitting-down times.

[0058] The feature F1 is extracted from the section of walking phase 42-54% of the walking waveform data Gx related to the time series data of the angular velocity in the sagittal plane (around the X-axis). The walking phase 42-54% is the section from the end of stance phase T3 to the early swing phase T4. The feature F1 mainly includes features related to the movement of the gastrocnemius muscle. FIG. 13 shows the verification result of the correlation between the feature F1 and the five standing and sitting times. The horizontal axis of the graph in FIG. 13 is normalized angular velocity. The correlation coefficient R between the feature F1 and the five standing and sitting times was -0.394.

[0059] The feature F2 is extracted from the 99-100% walking phase section of the walking waveform data Gy related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 99-100% walking phase corresponds to the end of the swing phase T7. The feature F2 mainly includes features related to the movements of the quadriceps, hamstrings, and anterior tibial muscles. Figure 14 shows the results of verifying the correlation between the feature F2 and the five-times standing-sit-down time. The horizontal axis of the graph in Figure 14 is normalized angular velocity. The correlation coefficient R between the feature F2 and the five-times standing-sit-down time was -0.387.

[0060] The feature F3 is extracted from the 10-12% gait phase section of the walking waveform data Gy related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 10-12% gait phase is the beginning of the mid-stance phase T2. The feature F3 mainly includes features related to the movements of the quadriceps, hamstrings, and gastrocnemius. Figure 15 shows the results of verifying the correlation between the feature F3 and the five-times sit-to-stand time. The horizontal axis of the graph in Figure 15 is normalized angular velocity. The correlation coefficient R between the feature F4 and the five-times sit-to-stand time was -0.387.

[0061] The feature F4 is extracted from the 99% walking phase section of the walking waveform data Ez, which is related to the time series data of the angle (posture angle) in the horizontal plane (around the Z-axis). The 99% walking phase is the end of the swing leg end phase T7. The feature F4 mainly includes features related to the movements of the quadriceps, hamstrings, and anterior tibial muscles. Figure 16 shows the results of verifying the correlation between the feature F4 and the five standing and sitting times. The horizontal axis of the graph in Figure 16 is the angle in the horizontal plane (around the Z-axis). The correlation coefficient R between the feature F4 and the five standing and sitting times was 0.379.

[0062] FIG. 17 is a conceptual diagram showing an example of inputting feature quantities F1 to F4 extracted from sensor data measured while the user is walking to an estimation model 151 constructed in advance to estimate the five-times standing-up and sitting-down time as the lower limb muscle strength. The estimation model 151 outputs the five-times standing-up and sitting-down time, which is a lower limb muscle strength index, in response to the input of feature quantities F1 to F4. For example, the estimation model 151 is generated by learning using teacher data in which the feature quantities F1 to F4 used to estimate the five-times standing-up and sitting-down time are explanatory variables and the five-times standing-up and sitting-down time is a target variable. As long as an estimation result regarding the five-times standing-up and sitting-down time, which is an index of the lower limb muscle strength, is output in response to the input of feature quantity data for estimating the five-times standing-up and sitting-down time, there is no limitation on the estimation result of the estimation model 151. For example, the estimation model 151 may be a model that estimates the five-times standing-up and sitting-down time using attribute data (age) as an explanatory variable in addition to the feature quantities F1 to F4 used to estimate the five-times standing-up and sitting-down time.

[0063] For example, an estimation model for estimating the five standing-up and sitting-down time using a multiple regression prediction method is stored in the storage unit 132. For example, the storage unit 132 stores parameters for estimating the five standing-up and sitting-down time T using the following formula 1. T=a1×F1+a2×F2+a3×F3+a4×F4+a0...(1) In the above formula 1, F1, F2, F3, and F4 are feature quantities for each walking phase cluster used to estimate the five standing and sitting times shown in the correspondence table of FIG. 12. a1, a2, a3, and a4 are coefficients multiplied by F1, F2, F3, and F4. a0 is a constant term. For example, a0, a1, a2, a3, and a4 are stored in the storage unit 132.

[0064] Next, the results of evaluating the estimation model 151 generated using the measurement data of the above-mentioned 62 subjects are shown. Here, a verification example (FIG. 18) in which the lower limb muscle strength (time to stand and sit down five times) is estimated using the attributes of the subjects (including walking speed) is compared with a verification example (FIG. 19) in which the lower limb muscle strength (time to stand and sit down five times) is estimated using the features of the subjects' gait. FIG. 18 and FIG. 19 show the results of testing the estimation model generated using the measurement data of 61 subjects by the LOSO (Leave-One-Subject-Out) method using the measurement data of the remaining one subject. FIG. 18 and FIG. 19 show the results of performing LOSO on all (62 subjects) subjects and matching the predicted values ​​by the test with the measured values ​​(true values). The LOSO test results are the intraclass correlation coefficients ICC, the mean absolute error MAE, and the coefficient of determination R 2 The intraclass correlation coefficient (ICC) was used to evaluate inter-examiner reliability.

[0065] Figure 18 shows the results of a comparative estimation model trained with training data that uses gender, age, height, weight, and walking speed as explanatory variables and five standing-sit times as a response variable. The comparative estimation model had an intraclass correlation coefficient ICC(2, 1) of 0.20, a mean absolute error MAE of 2.18, and a coefficient of determination R2 was 0.06.

[0066] 19 shows the verification result of the estimation model 151 of this embodiment, which was trained with training data using the features F1 to F4 and age as explanatory variables and the five standing and sitting times as the objective variable. The estimation model 151 of this embodiment has an intraclass correlation coefficient ICC(2, 1) of 0.503, a mean absolute error MAE of 1.87, and a coefficient of determination R 2 was 0.27. That is, the estimation model 151 of this embodiment is more reliable, has a smaller error, and the objective 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 objective 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 lower limb muscle strength estimation system 1 will be described with reference to the drawings. Here, we will individually describe gait measurement device 10 and lower limb muscle strength estimation device 13 included in lower limb muscle strength estimation system 1. Regarding gait measurement device 10, we will describe the operation of feature amount data generation unit 12 included in gait measurement device 10.

[0068] [Gait measurement device] Fig. 20 is a flowchart for explaining the operation of the feature amount data generating unit 12 included in the gait measurement device 10. In the explanation following the flowchart of Fig. 20, the feature amount data generating unit 12 will be described as the subject of the operation.

[0069] In FIG. 20, first, the feature amount data generating unit 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 section between successive heel strikes as gait waveform data for one gait cycle.

[0071] Next, the feature amount data generating unit 12 normalizes the gait waveform data for the extracted one step cycle (step S103). The feature amount data generating unit 12 normalizes the gait waveform data for the one step cycle to a gait cycle of 0 to 100% (first normalization). Furthermore, the feature amount data generating unit 12 normalizes the ratio of the stance phase and the swing phase of the gait waveform data for the first normalized one step cycle to 60:40 (second normalization).

[0072] Next, the feature data generating unit 12 extracts feature amounts from the walking phases used for estimating the lower limb muscle strength from the normalized walking waveform (step S104). For example, the feature data generating unit 12 extracts feature amounts to be input to a previously constructed estimation model.

[0073] Next, the feature data generating unit 12 generates a feature for each walking phase cluster using the extracted feature (step S105).

[0074] Next, the feature amount data generating unit 12 integrates the feature amounts for each walking phase cluster to generate feature amount data for one step gait cycle (step S106).

[0075] Next, the feature amount data generating unit 12 outputs the generated feature amount data to the lower limb muscle strength estimating device 13 (step S107).

[0076] [Lower limb muscle strength estimation device] Fig. 21 is a flowchart for explaining the operation of the lower limb muscle strength estimation device 13. In the explanation following the flowchart of Fig. 21, the lower limb muscle strength estimation device 13 will be described as the subject of the operation.

[0077] In FIG. 21, first, the lower limb muscle strength estimation device 13 acquires feature amount data generated using sensor data related to leg movements (step S131).

[0078] Next, the lower limb muscle strength estimation device 13 inputs the acquired feature amount data into an estimation model for estimating lower limb muscle strength (five standing-sit down times) (step S132).

[0079] Next, the lower limb muscle strength estimation device 13 estimates the lower limb muscle strength of the user according to the output (estimated value) from the estimation model (step S133). For example, the lower limb muscle strength estimation device 13 estimates the five standing-sitting times of the user as the lower limb muscle strength.

[0080] Next, the lower limb muscle strength estimation device 13 outputs information on the estimated lower limb muscle strength (step S134). For example, the lower limb muscle strength is output to a terminal device (not shown) carried by the user. For example, the lower limb muscle strength is output to a system that executes a process using the lower limb muscle strength.

[0081] (Examples) Next, application examples according to the present embodiment will be described with reference to the drawings. In the following application examples, a function of a lower limb muscle strength estimation device 13 installed in a mobile terminal carried by a user is used to estimate lower limb muscle strength using feature amount data measured by a gait measurement device 10 placed on a shoe.

[0082] Fig. 22 is a conceptual diagram showing an example of displaying an estimation result by the lower limb muscle strength estimation device 13 on the screen of a mobile terminal 160 carried by a user walking wearing shoes 100 on which a gait measurement device 10 is placed. Fig. 22 shows an example of displaying information corresponding to an estimation result of lower limb muscle strength using feature amount data corresponding to sensor data measured while the user is walking on the screen of the mobile terminal 160.

[0083] FIG. 22 is an example in which information according to an estimated value of the five-times stand-up and sitting time, which is the lower limb muscle strength, is displayed on the screen of the mobile terminal 160. In the example of FIG. 22, the estimated value of the five-times stand-up and sitting time is displayed on the display unit of the mobile terminal 160 as the estimation result of the lower limb muscle strength. Also, in the example of FIG. 22, information on the estimation result of the lower limb muscle strength, "Your lower limb muscle strength is declining," is displayed on the display unit of the mobile terminal 160 in accordance with the estimated value of the five-times stand-up and sitting time, which is the lower limb muscle strength. Also, in the example of FIG. 22, recommendation information according to the estimation result of the lower limb muscle strength, "Training A is recommended. Please watch the video below," is displayed on the display unit of the mobile terminal 160 in accordance with the estimated value of the five-times stand-up and sitting time, which is the lower limb muscle strength. A user who has confirmed the information displayed on the display unit of the mobile terminal 160 can practice training that leads to an increase in lower limb muscle strength by exercising by referring to the video of training A in accordance with the recommendation information.

[0084] As described above, the lower limb muscle strength estimation system of this embodiment includes a gait measurement device and a lower limb muscle strength estimation device. The gait measurement device includes a sensor and a feature amount data generation unit. The sensor has an acceleration sensor and an angular velocity sensor. The sensor measures 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 amount data generation unit. The feature amount data generation unit acquires time series data of the sensor data related to foot movement. The feature amount data generation unit extracts gait waveform data for one step cycle from the time series data of the sensor data. The feature amount data generation unit normalizes the extracted gait waveform data. The feature amount data generation unit extracts features used for estimating lower limb muscle strength from the normalized gait waveform data, from a gait phase cluster formed by at least one gait phase that is continuous in time. The feature amount data generation unit generates feature amount data including the extracted features. The feature amount data generating unit outputs the generated feature amount data.

[0085] The lower limb muscle strength 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 lower limb muscle strength, extracted from sensor data related to the user's foot movement. The storage unit stores an estimation model that outputs a lower limb muscle strength index according to input of the feature data. The estimation unit inputs the acquired feature data to the estimation model to estimate the user's lower limb muscle strength. The output unit outputs information related to the estimated lower limb muscle strength.

[0086] The lower limb muscle strength estimation system of this embodiment estimates the lower limb muscle strength of a user by using feature amounts extracted from sensor data related to the movement of the user's legs. Therefore, the lower limb muscle strength estimation system of this embodiment can appropriately estimate the lower limb muscle strength in daily life without using any equipment for measuring the lower limb muscle strength.

[0087] In one aspect of the present embodiment, the data acquisition unit acquires feature amount 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 amount data including feature amounts used to estimate a score value of a sit-to-stand test as a lower limb muscle strength index. According to this aspect, by using the sensor data related to foot movement, it is possible to appropriately estimate lower limb muscle strength in daily life without using a tool for measuring lower limb muscle strength.

[0088] In one aspect of this embodiment, the storage unit stores an estimation model generated by learning using teacher data related to multiple subjects. The estimation model is generated by learning using teacher data in which feature values ​​used to estimate the lower limb muscle strength index are explanatory variables and the lower limb muscle strength indexes of the multiple subjects are objective variables. The estimation unit inputs feature value data acquired about the user to the estimation model. The estimation unit estimates the user's lower limb muscle strength according to the user's lower limb muscle strength index output from the estimation model. According to this aspect, lower limb muscle strength can be appropriately estimated in daily life without using an instrument for measuring lower limb muscle strength.

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

[0090] In one aspect of the present embodiment, the storage unit stores an estimation model generated by learning using teacher data related to a plurality of subjects. The estimation model is a model generated by learning using teacher data in which feature values ​​extracted from gait waveform data of a plurality of subjects are explanatory variables and lower limb muscle strength indices of a plurality of subjects are objective variables. For example, feature values ​​related to the quadriceps, hamstrings, and gastrocnemius extracted from the beginning of mid-stance phase are included in the explanatory variables. For example, feature values ​​related to the activity of the gastrocnemius extracted from the section from the end of stance phase to the early swing phase are included in the explanatory variables. For example, feature values ​​related to the activity of the quadriceps, hamstrings, and tibialis anterior extracted from the end of swing phase are included in the explanatory variables. The estimation unit inputs feature value data acquired according to the user's walking to the estimation model. The estimation unit estimates the user's lower limb muscle strength according to the user's lower limb muscle strength index output from the estimation model. According to this aspect, by using an estimation model that has learned features according to muscle activity that affects lower limb muscle strength, it is possible to estimate lower limb muscle strength that is more suited to physical activity.

[0091] In one aspect of the present embodiment, the storage unit stores an estimation model generated by learning using teacher data in which a plurality of feature quantities extracted from gait waveform data of a plurality of subjects are explanatory variables, and the lower limb muscle strength of the subject with respect to the lower limb muscle strength index is a response variable. For example, the explanatory variables include feature quantities extracted from the beginning of the mid-stance phase and the end of the end-swing phase of the gait waveform data of the angular velocity in the coronal plane. For example, the explanatory variables include feature quantities extracted from the section from the end-stance phase to the early-swing phase of the gait waveform data of the angular velocity in the sagittal plane. For example, the explanatory variables include feature quantities extracted from the end-swing phase of the angle in the horizontal plane. The data acquisition unit acquires feature quantity data including feature quantities extracted according to the user's gait. For example, the data acquisition unit acquires feature quantities from the beginning of the mid-stance phase and the end of the end-swing phase of the gait waveform data of the angular velocity in the coronal plane. For example, the data acquisition unit acquires feature quantities from the section from the end-stance phase to the early-swing phase of the gait waveform data of the angular velocity in the sagittal plane. For example, the data acquisition unit acquires a feature value of the end of the swing phase of the gait waveform data of an angle in a horizontal plane. The estimation unit inputs the acquired feature value data to an estimation model. The estimation unit estimates the user's lower limb muscle strength according to the user's lower limb muscle strength index output from the estimation model. According to this aspect, by using an estimation model that has learned features extracted from gait waveform data that includes features according to muscle activity that affects the lower limb muscle strength, it is possible to estimate lower limb muscle strength that is more suited to physical activity using sensor data related to foot movement.

[0092] In one aspect of the present embodiment, the lower limb muscle strength estimation device is implemented in a terminal device having a screen viewable by a user. For example, the lower limb muscle strength estimation device displays information on the lower limb muscle strength estimated according to the user's foot movement on the screen of the terminal device. For example, the lower limb muscle strength estimation device displays recommendation information according to the lower limb muscle strength estimated according to the user's foot movement on the screen of the terminal device. For example, the lower limb muscle strength estimation device displays a video on the screen of the terminal device as the recommendation information according to the lower limb muscle strength estimated according to the user's foot movement, which is a training video for training a body part related to the lower limb muscle strength. According to this aspect, the lower limb muscle strength estimated according to the feature amount extracted from the sensor data related to the user's foot movement is displayed on a screen viewable by the user, so that the user can confirm information according to his or her own lower limb muscle strength.

[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 lower limb muscle strength according to input of feature values ​​by learning using feature value data extracted from sensor data measured by a gait measurement device.

[0094] (composition) FIG. 23 is a block diagram showing an example of the configuration of the learning system 2 according to this embodiment. The learning system 2 includes a gait measurement device 20 and a learning device 25. The gait measurement device 20 and the learning device 25 may be connected by wire or wirelessly. The gait measurement device 20 and the learning device 25 may be configured as a single device. Furthermore, the learning system 2 may be configured by only the learning device 25, excluding the gait measurement device 20 from the configuration of the learning system 2. Although only one gait measurement device 20 is illustrated in FIG. 23, one gait measurement device 20 may be disposed on each of the left and right feet (two in total). Furthermore, the learning device 25 may be configured to execute learning using feature amount data that is generated in advance by the gait measurement device 20 and stored in a database, without being connected to the gait measurement device 20.

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

[0096] The learning device 25 receives feature amount data from the gait measurement device 20. When feature amount data stored in a database (not shown) is used, the learning device 25 receives the feature amount data from the database. The learning device 25 executes learning using the received feature amount data. For example, the learning device 25 learns teacher data in which feature amount data extracted from walking waveform data of a plurality of subjects is used as an explanatory variable and a value related to lower limb muscle strength corresponding to the feature amount data is used as a target variable. There is no particular limitation on the learning algorithm executed by the learning device 25. The learning device 25 generates an estimation model learned using teacher data related to a plurality of subjects. The learning device 25 stores the generated estimation model. The estimation model learned by the learning device 25 may be stored in an external storage device of the learning device 25.

[0097] [Learning device] Next, details of the learning device 25 will be described with reference to the drawings. Fig. 24 is a block diagram showing an example of a 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 the feature amount data from gait measurement device 20 via a wired connection such as a cable, or may receive the feature amount data from gait measurement device 20 via wireless communication. For example, receiving unit 251 is configured to receive the feature amount data from gait measurement device 20 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of receiving unit 251 may conform to a standard 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 executes learning using the acquired feature amount data. For example, the learning unit 253 learns a data set in which the feature amount data extracted from the sensor data measured according to the movement of the subject's feet is used as an explanatory variable, and the five standing and sitting times of the subject are used as an objective variable, as teacher data. For example, the learning unit 253 generates an estimation model that estimates the five standing and sitting times according to the input of feature amount data, which is learned about multiple subjects. For example, the learning unit 253 generates an estimation model according to attribute data (age). For example, the learning unit 253 generates an estimation model that estimates the five standing and sitting times as lower limb muscle strength, using the feature amount data extracted from the sensor data measured according to the movement of the subject's feet and the attribute data (age) of the subject as explanatory variables. The learning unit 253 stores the estimation models learned about 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 subject that generated the feature amount data according to the feature amount data. There is no particular limitation on the learning algorithm performed by the learning unit 253.

[0101] The learning unit 253 may execute learning using the walking waveform data for one step cycle as explanatory variables. For example, the learning unit 253 executes supervised learning using the walking waveform data of the acceleration in three axial directions, the angular velocity around the three axes, and the angle around the three axes (posture angle) as explanatory variables, and the correct value of the lower limb muscle strength to be estimated as the objective variable. For example, when the walking phase is set in 1% increments in a walking cycle of 0 to 100%, the learning unit 253 executes learning using 909 explanatory variables.

[0102] Fig. 25 is a conceptual diagram for explaining learning for generating an estimation model. Fig. 25 is a conceptual diagram showing an example of learning by the learning unit 253 using a data set of features F1 to F4, which are explanatory variables, and a five-time standing-up and sitting-down time (lower limb muscle strength index), which is a response variable, as teacher data. For example, the learning unit 253 learns data on a plurality of subjects, and generates an estimation model that outputs an output (estimated value) related to the five-time standing-up and sitting-down time (lower limb muscle strength index) in response to an input of features extracted from sensor data.

[0103] The storage unit 255 stores an estimation model trained on a plurality of subjects. For example, the storage unit 255 stores an estimation model for estimating lower limb muscle strength trained on a plurality of subjects. For example, the estimation model stored in the storage unit 255 is used for estimating lower limb muscle strength by the lower limb muscle strength 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 data of sensor data related to foot movement. The gait measurement device extracts gait waveform data for a step cycle from the time series data of the sensor data, and normalizes the extracted gait waveform data. The gait measurement device extracts, from the normalized gait waveform data, feature amounts used for estimating the user's lower limb muscle strength from a walking phase cluster constituted by at least one walking phase that is consecutive in time. The gait measurement device generates feature amount data including the extracted feature amount. 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 executes learning using the feature data. The learning unit generates an estimation model that outputs lower limb muscle strength in response to input of a feature (second feature) of a walking phase cluster extracted from time-series data of sensor data measured in association with the user's walking. 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 the gait measurement device. Therefore, according to this aspect, it is possible to generate an estimation model that allows appropriate estimation of lower limb muscle strength in daily life without using any equipment for measuring lower limb muscle strength.

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

[0108] 26 is a block diagram showing an example of the configuration of a lower limb muscle strength estimation device 33 according to this embodiment. The lower limb muscle strength 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 amount data including feature amounts used to estimate the user's lower limb muscle strength index, extracted from sensor data related to the user's leg movements. The storage unit 332 stores an estimation model that outputs a lower limb muscle strength index according to input of the feature amount data. The estimation unit 333 inputs the acquired feature amount data to the estimation model, and estimates the user's lower limb muscle strength according to the lower limb muscle strength index output from the estimation model. The output unit 335 outputs information related to the estimated lower limb muscle strength.

[0110] As described above, in this embodiment, the feature values ​​extracted from the sensor data related to the user's leg movements are used to estimate the user's lower limb muscle strength. Therefore, according to this embodiment, the lower limb muscle strength can be appropriately estimated in daily life without using a tool for measuring the lower limb muscle strength.

[0111] (Hardware) Here, a hardware configuration for executing the control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 27 as an example. Note that the information processing device 90 in Fig. 27 is an example of a configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0112] As shown in Fig. 27, 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. 27, the 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. In addition, the processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

[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 a program is loaded. The processor 91 loads a program stored in the auxiliary memory device 93 or the like in the main memory device 92. The main memory device 92 is realized by a volatile memory such as a dynamic random access memory (DRAM). Furthermore, a non-volatile memory such as a magnetoresistive random access memory (MRAM) 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 a flash memory. Note that it is also possible to configure the main storage device 92 to store various data and omit 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 through 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, a mouse, and a touch panel may be connected to the information processing device 90 as necessary. These input devices are used to input information and settings. When a touch panel is used as an input device, the display screen of the display device may also serve as an 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 be equipped with a display device for displaying information. When the display device is equipped, the information processing device 90 is preferably equipped with a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0119] The information processing device 90 may also be provided with a drive device. The drive device mediates 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, and the like. 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 the control and processing according to each embodiment of the present invention. The hardware configuration in FIG. 27 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present invention. In addition, a program for causing a computer to execute the control and processing according to each embodiment is also included in the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also included in the scope of the present invention. The recording medium can be realized, for example, by an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may be realized by a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. In addition, the recording medium may be realized by 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. Furthermore, the components of each embodiment 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-mentioned 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 is not limited to, the following supplementary notes. (Appendix 1) a data acquisition unit that acquires feature data including feature values ​​used to estimate a lower limb muscle strength of the user, the feature values ​​being extracted from sensor data related to a user's foot movement; a storage unit that stores an estimation model that outputs a lower limb muscle strength index according to input of the feature amount data; an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; and an output unit that outputs information regarding the estimated lower limb muscle strength of the user. (Appendix 2) The data acquisition unit is A lower limb muscle strength estimation device as described in Appendix 1, which acquires the feature data including features used to estimate a performance value of a sit-to-stand test as the lower limb muscle strength index, the feature data being extracted from walking waveform data generated using time-series data of the sensor data regarding foot movement. (Appendix 3) The storage unit is storing the estimation model generated by learning using teacher data in which the feature quantities used for estimating the lower limb muscle strength indexes are explanatory variables and the lower limb muscle strength indexes of the plurality of subjects are objective variables for a plurality of subjects; The estimation unit is 3. A lower limb muscle strength estimation device as described in Appendix 2, which inputs the feature data acquired about the user into the estimation model, and estimates the lower limb muscle strength of the user based on the lower limb muscle strength index of the user output from the estimation model. (Appendix 4) The storage unit is storing the estimation model trained using explanatory variables including the ages of the multiple subjects; The estimation unit is 4. A lower limb muscle strength estimation device as described in claim 3, which inputs the feature data and age of the user into the estimation model, and estimates the lower limb muscle strength of the user based on the lower limb muscle strength index of the user output from the estimation model. (Appendix 5) The storage unit is the estimation model is generated by learning using teacher data in which, for the walking waveform data of the plurality of subjects, explanatory variables are feature amounts related to the quadriceps, hamstrings, and gastrocnemius extracted from the beginning of mid-stance phase, feature amounts related to the activity of the gastrocnemius extracted from the section from final stance phase to early swing phase, and feature amounts related to the activity of the quadriceps, hamstrings, and tibialis anterior extracted from the end of final swing phase, and the lower limb muscle strength indexes of the plurality of subjects are objective variables; and The estimation unit is 5. A lower limb muscle strength estimation device according to claim 3 or 4, which inputs the feature data acquired in response to the user's walking into the estimation model, and estimates the lower limb muscle strength of the user in response to the lower limb muscle strength index of the user output from the estimation model. (Appendix 6) The storage unit is the estimation model is generated by learning using teacher data in which explanatory variables are a feature value extracted from an early part of a mid-stance phase and an end part of a final swing phase of the gait waveform data of an angular velocity in a coronal plane, a feature value extracted from a section from a final stance phase to an early swing phase of the gait waveform data of an angular velocity in a coronal plane, and a feature value extracted from an end part of a final swing phase of the gait waveform data of an angle in a horizontal plane, and the lower limb muscle strength index of the plurality of subjects is used as a response variable; and The data acquisition unit is Acquire the feature amount data, which includes a feature amount of an early part of a mid-stance phase and an end part of a final swing phase of the gait waveform data of an angular velocity in a coronal plane, a feature amount of a section from a final stance phase to an early swing phase of the gait waveform data of an angular velocity in a coronal plane, and a feature amount of an end part of a final swing phase of the gait waveform data of an angle in a horizontal plane, which are extracted in accordance with the user's walking; The estimation unit is A lower limb muscle strength estimation device as described in Appendix 5, which inputs the acquired feature data into the estimation model, and estimates the lower limb muscle strength of the user based on the lower limb muscle strength index of the user output from the estimation model. (Appendix 7) The estimation unit is estimating information related to the lower limb muscle strength of the user according to the lower limb muscle strength index estimated for the user; The output unit is 7. A lower limb muscle strength estimation device according to any one of appendix 3 to 6, which outputs information related to the estimated lower limb muscle strength. (Appendix 8) A lower limb muscle strength estimation device according to any one of appendix 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 a step cycle from the time series data of the sensor data, normalizes the extracted gait waveform data, extracts feature values ​​used for estimating the lower limb muscle strength from a gait phase cluster formed by at least one gait phase that is consecutive in time, generates feature data including the extracted feature values, and outputs the generated feature data to the lower limb muscle strength estimation device. (Appendix 9) The lower limb muscle strength estimation device includes: implemented in a terminal device having a screen viewable by the user, 9. The lower limb muscle strength estimation system according to claim 8, wherein information regarding the lower limb muscle strength estimated according to the movement of the user's feet is displayed on a screen of the terminal device. (Appendix 10) The lower limb muscle strength estimation device includes: A lower limb muscle strength estimation system as described in Appendix 9, which displays recommendation information according to the lower limb muscle strength estimated according to the user's foot movement on a screen of the terminal device. (Appendix 11) The lower limb muscle strength estimation device includes: A lower limb muscle strength estimation system as described in Appendix 10, which displays on a screen of the terminal device a video related to training for training a body part related to the lower limb muscle strength as the recommendation information corresponding to the lower limb muscle strength estimated according to the movement of the user's feet. (Appendix 12) The computer Acquire feature data including features used to estimate a lower limb muscle strength of the user, the feature data being extracted from sensor data related to the user's foot movement; inputting the acquired feature amount data into an estimation model that outputs a lower limb muscle strength index according to the input of the feature amount data; estimating the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; A lower limb muscle strength estimation method that outputs information regarding the estimated lower limb muscle strength of the user. (Appendix 13) A process of acquiring feature data including features used to estimate a lower limb muscle strength of a user, the feature data being extracted from sensor data related to the user's foot movement; inputting the acquired feature amount data into an estimation model that outputs a lower limb muscle strength index according to the input of the feature amount data; A process of estimating the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; and outputting information relating to the estimated lower limb muscle strength of the user. [Explanation of symbols]

[0124] 1. Lower limb muscle strength estimation system 2. Learning System 10, 20 Gait measurement device 11 Sensors 12 Feature data generation unit 13. Lower limb muscle strength 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 data used to estimate a lower limb muscle strength of a user, the feature data being extracted from sensor data related to the user's foot movement; an estimation means for inputting the acquired feature amount data into an estimation model that outputs a lower limb muscle strength index according to an input of the feature amount data, and estimating the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; and an output means for outputting information related to the estimated lower limb muscle strength of the user, The data acquisition means includes: Acquire the feature data including the feature used to estimate a score value of a sit-to-stand test as the lower limb muscle strength index, the feature data being extracted from walking waveform data generated using time-series data of the sensor data related to foot movement; The estimation means includes: a feature amount related to the quadriceps, hamstrings, and gastrocnemius extracted from the beginning of mid-stance phase, a feature amount related to gastrocnemius activity extracted from the section from end-stance phase to early-swing phase, and a feature amount related to the quadriceps, hamstrings, and tibial anterior activity extracted from the end of end-swing phase as explanatory variables for the walking waveform data of a plurality of subjects, and the feature amount acquired in accordance with the walking of the user is input into the estimation model generated by learning using teacher data in which the lower limb muscle strength index of the plurality of subjects is a target variable; and the lower limb muscle strength of the user is estimated in accordance with the lower limb muscle strength index of the user output from the estimation model.

2. A storage means for storing the estimation model generated by learning using teacher data in which feature quantities used to estimate the lower limb muscle strength indexes of a plurality of subjects are explanatory variables and the lower limb muscle strength indexes of the plurality of subjects are objective variables, The estimation means includes: The lower limb muscle strength estimation device according to claim 1 , wherein the feature data acquired regarding the user is input into the estimation model, and the lower limb muscle strength of the user is estimated based on the lower limb muscle strength index of the user output from the estimation model.

3. The storage means includes: storing the estimation model trained using explanatory variables including the ages of the multiple subjects; The estimation means includes: The lower limb muscle strength estimation device according to claim 2 , wherein the feature data and age of the user are input into the estimation model, and the lower limb muscle strength of the user is estimated based on the lower limb muscle strength index of the user output from the estimation model.

4. The storage means includes: the estimation model is generated by learning using teacher data in which, for the walking waveform data of the plurality of subjects, explanatory variables are feature amounts related to the quadriceps, hamstrings, and gastrocnemius extracted from the beginning of mid-stance phase, feature amounts related to the activity of the gastrocnemius extracted from the section from final stance phase to early swing phase, and feature amounts related to the activity of the quadriceps, hamstrings, and tibialis anterior extracted from the end of final swing phase, and the lower limb muscle strength indexes of the plurality of subjects are objective variables; and The estimation means includes:

4. The lower limb muscle strength estimation device according to claim 2 or 3, wherein the feature data acquired in response to the user's walking is input into the estimation model, and the lower limb muscle strength of the user is estimated in response to the lower limb muscle strength index of the user output from the estimation model.

5. The storage means includes: the estimation model is generated by learning using teacher data in which explanatory variables are a feature value extracted from an early part of a mid-stance phase and an end part of a final swing phase of the gait waveform data of an angular velocity in a coronal plane, a feature value extracted from a section from a final stance phase to an early swing phase of the gait waveform data of an angular velocity in a sagittal plane, and a feature value extracted from an end part of a final swing phase of the gait waveform data of an angle in a horizontal plane, and the lower limb muscle strength index of the plurality of subjects is used as a response variable; and The data acquisition means includes: The feature amount data includes a feature amount of an angular velocity in a coronal plane at an early stage of a mid-stance phase and an end stage of a final swing phase of the gait waveform data, a feature amount of an angular velocity in a sagittal plane at a section from the final stance phase to the early swing phase of the gait waveform data, and a feature amount of an angle in a horizontal plane at a late stage of a final swing phase of the gait waveform data, which are extracted in accordance with the user's walking; The estimation means includes: The lower limb muscle strength estimation device according to claim 4 , wherein the acquired feature data is input into the estimation model, and the lower limb muscle strength of the user is estimated based on the lower limb muscle strength index of the user output from the estimation model.

6. The lower limb muscle strength estimation device according to any one of claims 1 to 5, a gait measurement device having a feature data generation means for acquiring time series data of the sensor data including gait features, extracting gait waveform data for a step cycle from the time series data of the sensor data, normalizing the extracted gait waveform data, extracting feature data used for estimating the lower limb muscle strength from a walking phase cluster formed by at least one walking phase that is temporally consecutive, generating feature data including the extracted feature data, and outputting the generated feature data to the lower limb muscle strength estimation device.

7. The lower limb muscle strength estimation device includes: implemented in a terminal device having a screen viewable by the user, The lower limb muscle strength estimation system according to claim 6 , wherein information regarding the lower limb muscle strength estimated in accordance with the movement of the user's feet is displayed on a screen of the terminal device.

8. The lower limb muscle strength estimation device includes: The lower limb muscle strength estimation system according to claim 7 , wherein recommendation information corresponding to the lower limb muscle strength estimated according to the foot movement of the user is displayed on a screen of the terminal device.

9. The computer Acquire feature data including features used to estimate a lower limb muscle strength of the user, the feature data being extracted from sensor data related to the user's foot movement; inputting the acquired feature amount data into an estimation model that outputs a lower limb muscle strength index according to the input of the feature amount data; estimating the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; Outputting information related to the estimated lower limb muscle strength of the user; In the acquisition, Acquire the feature data including the feature used to estimate a score value of a sit-to-stand test as the lower limb muscle strength index, the feature data being extracted from walking waveform data generated using time-series data of the sensor data related to foot movement; In the above estimation, a feature amount extracted from the beginning of mid-stance phase, a feature amount extracted from the section from end-stance phase to early-swing phase, and a feature amount extracted from the end-swing phase of the quadriceps, hamstrings, and tibial anterior muscle activity extracted from the end of end-swing phase as explanatory variables for the walking waveform data of a plurality of subjects, and the feature amount data acquired in accordance with the walking of the user is input into the estimation model generated by learning using teacher data in which the lower limb muscle strength index of the plurality of subjects is a target variable; and the lower limb muscle strength of the user is estimated in accordance with the lower limb muscle strength index of the user output from the estimation model.

10. A computer comprising: A process of acquiring feature data including features used to estimate a lower limb muscle strength of a user, the feature data being extracted from sensor data related to the user's foot movement; inputting the acquired feature amount data into an estimation model that outputs a lower limb muscle strength index according to the input of the feature amount data; A process of estimating the lower limb muscle strength of the user according to the lower limb muscle strength index output from the estimation model; outputting information related to the estimated lower limb muscle strength of the user; In the acquiring process, A process of acquiring feature data including features used to estimate a score value of a sit-to-stand test as the lower limb muscle strength index, the feature data being extracted from walking waveform data generated using time-series data of the sensor data related to foot movement; In the estimation process, and inputting the feature data acquired in response to the walking of the user into the estimation model generated by learning using teacher data in which, with respect to the walking waveform data of a plurality of subjects, explanatory variables include feature values ​​related to the quadriceps, hamstrings, and gastrocnemius extracted from an early stage of mid-stance phase, feature values ​​related to gastrocnemius activity extracted from a section from final stance phase to early swing phase, and feature values ​​related to the quadriceps, hamstrings, and tibial anterior activity extracted from an end of final swing phase, and the lower limb muscle strength index of the plurality of subjects is an objective variable; and estimating the lower limb muscle strength of the user in response to the lower limb muscle strength index of the user output from the estimation model.

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