Muscle strength evaluation device, muscle strength evaluation system, muscle strength evaluation method, and program

Abstract

To evaluate muscle strength of a fall risk-associated muscle according to gait in daily life, this muscle strength evaluation device is provided with: a data acquisition part for acquiring feature quantity data derived from sensor data related to movement of a user's foot and including a feature quantity to be used in estimating muscle strength of a fall risk-associated muscle being evaluated; a storage part for storing an estimation model that outputs a muscle strength index for the muscle being evaluated according to input of feature quantity data; an evaluation part which inputs the acquired feature quantity in the estimation model and evaluates the muscle strength of the user's muscle that is being evaluated according to the muscle strength index output from the estimation model; and an output part for outputting information concerning the evaluation results related to the muscle strength of the user's muscle that is being evaluated.

Description

[Technical Field]

[0001] The present disclosure relates to a muscle strength evaluation device and the like that evaluates muscle strength using sensor data related to leg movement. [Background technology]

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

[0003] Patent Document 1 discloses a device for detecting foot abnormalities based on the walking characteristics of a walker. The device in Patent Document 1 uses data acquired from a sensor attached to the footwear to extract gait features that are characteristic of the gait of a walker wearing the footwear. The device in Patent Document 1 detects abnormalities in a walker wearing the footwear based on the extracted gait features. For example, the device in Patent Document 1 extracts characteristic parts related to hallux valgus from gait waveform data for one stride cycle. The device in Patent Document 1 estimates the progression of hallux valgus using the gait features of the extracted characteristic parts.

[0004] Falls can cause various injuries to elderly people. Declines in physical ability, such as muscle weakness, can be a factor in the risk of falling. The causes of muscle weakness vary from person to person. Therefore, measures to address muscle weakness tailored to each individual are necessary. If the muscles related to walking can be evaluated according to gait, training that will lead to a reduction in the risk of falling can be recommended to each individual.

[0005] Patent Document 2 discloses a training support system in which an expert provides individual instruction to a user who is training at home. The system of Patent Document 2 stores the user's physical ability test results, user identification information, and time information related to the test administration in association with each other. The system of Patent Document 2 evaluates the variation between multiple test results with respect to the time information related to the administration of multiple tests. The system of Patent Document 2 notifies the user that the training information should be updated based on the variation between the test results. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication No. 2021 / 140658 [Patent Document 2] Japanese Patent Application Laid-Open No. 2013-066672 Summary of the Invention [Problem to be solved by the invention]

[0007] The method of Patent Document 1 estimates the progression of hallux valgus using gait feature quantities of characteristic parts extracted from data acquired by a sensor attached to footwear. Patent Document 1 does not disclose estimating fall susceptibility using gait feature quantities of characteristic parts extracted from data acquired by a sensor attached to footwear.

[0008] According to the method of Patent Document 2, it is possible to prescribe training according to the decline in the user's physical ability. However, the method of Patent Document 2 cannot identify the muscles that are the cause of the decline in physical ability. Therefore, the method of Patent Document 2 cannot prescribe appropriate training to strengthen the muscles that are the cause of the decline in physical ability.

[0009] An object of the present disclosure is to provide a muscle strength evaluation device and the like that can evaluate the muscle strength of muscles related to the risk of falling according to gait in daily life. [Means for solving the problem]

[0010] A muscle strength evaluation device according to one aspect of the present disclosure includes a data acquisition unit that acquires feature data including feature values ​​used to estimate muscle strength of muscles to be evaluated that are related to the risk of falling, extracted from sensor data related to the movement of a user's feet; a memory unit that stores an estimation model that outputs muscle strength indicators for the muscles to be evaluated in response to input of the feature data; an evaluation unit that inputs the acquired feature data into the estimation model and evaluates the muscle strength of the user's muscles to be evaluated in response to the muscle strength indicators output from the estimation model; and an output unit that outputs information related to the evaluation results related to the muscle strength of the user's muscles to be evaluated.

[0011] In one aspect of the muscle strength evaluation method of the present disclosure, feature data including features used to estimate the muscle strength of muscles to be evaluated that are related to the risk of falling is obtained from sensor data related to the movement of a user's feet, the obtained feature data is input to an estimation model that outputs a muscle strength index of the muscles to be evaluated in response to the input of the feature data, the muscle strength of the user's muscles to be evaluated is evaluated in response to the muscle strength index output from the estimation model, and information related to the evaluation result regarding the muscle strength of the user's muscles to be evaluated is output.

[0012] A program according to one aspect of the present disclosure causes a computer to perform the following processes: acquiring feature data including features used to estimate the muscle strength of muscles to be evaluated that are related to the risk of falling, the feature data being extracted from sensor data related to the movement of a user's feet; inputting the acquired feature data into an estimation model that outputs a muscle strength index for the muscles to be evaluated in response to the input of the feature data; evaluating the muscle strength of the user's muscles to be evaluated in response to the muscle strength index output from the estimation model; and outputting information related to the evaluation results regarding the muscle strength of the user's muscles to be evaluated. [Effects of the Invention]

[0013] According to the present disclosure, an object of the present disclosure is to provide a muscle strength evaluation device, etc., that can evaluate the muscle strength of muscles related to the risk of falling according to gait in daily life. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram showing an example of the configuration of a muscle strength evaluation system according to a first embodiment. [Figure 2] 1 is a block diagram showing an example of the configuration of a gait measurement device included in the muscle strength evaluation system according to the first embodiment. FIG. [Figure 3] FIG. 1 is a conceptual diagram showing an example of the arrangement of a gait measurement device according to a first embodiment. [Figure 4] FIG. 2 is a conceptual diagram for explaining an example of the relationship between a local coordinate system and a world coordinate system set in the gait measurement device according to the first embodiment. [Figure 5] FIG. 1 is a conceptual diagram for explaining a human body surface used in explaining the gait measurement device according to the first embodiment. [Figure 6] FIG. 1 is a conceptual diagram for explaining a walking cycle used in explaining the gait measurement device according to the first embodiment. [Figure 7] FIG. 1 is a conceptual diagram for explaining gait parameters used in explaining the gait measurement device according to the first embodiment. [Figure 8] 3 is a graph for explaining an example of time-series data of sensor data measured by the gait measurement device according to the first embodiment. [Figure 9] FIG. 3 is a diagram for explaining an example of normalization of gait waveform data extracted from time-series data of sensor data measured by the gait measurement device according to the first embodiment. [Figure 10] FIG. 2 is a conceptual diagram for explaining an example of a walking phase cluster from which a feature data generation device of the gait measurement device according to the first embodiment extracts feature amounts. [Figure 11] 1 is a block diagram showing an example of the configuration of a muscle strength evaluation device included in a muscle strength evaluation system according to a first embodiment. [Figure 12] FIG. 2 is a conceptual diagram for explaining items related to muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 13]1 is a table summarizing feature amounts related to the total muscle strength (grip strength) of the whole body associated with the muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 14] 1 is a table summarizing feature quantities related to dynamic balance associated with muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 15] 1 is a table summarizing feature quantities related to lower limb muscle strength associated with muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 16] 1 is a table summarizing feature quantities related to mobility ability associated with muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 17] 1 is a table summarizing feature quantities related to static balance associated with muscles to be evaluated in the muscle strength evaluation system according to the first embodiment. [Figure 18] FIG. 2 is a conceptual diagram for explaining an example of a muscle to be evaluated by the muscle strength evaluation device included in the muscle strength evaluation system according to the first embodiment. [Figure 19] 1 is a conceptual diagram showing an example of estimation of a muscle strength score (muscle strength index) by a muscle strength evaluation device included in a muscle strength evaluation system according to the first embodiment. FIG. [Figure 20] 5 is a flowchart for explaining an example of the operation of the gait measurement device included in the muscle strength evaluation system according to the first embodiment. [Figure 21] 4 is a flowchart for explaining an example of the operation of the muscle strength evaluation device included in the muscle strength evaluation system according to the first embodiment. [Figure 22] FIG. 2 is a conceptual diagram for explaining an application example of the muscle strength evaluation system according to the first embodiment. [Figure 23] FIG. 2 is a conceptual diagram for explaining an application example of the muscle strength evaluation system according to the first embodiment. [Figure 24] FIG. 2 is a conceptual diagram for explaining an application example of the muscle strength evaluation system according to the first embodiment. [Figure 25] FIG. 10 is a block diagram showing an example of the configuration of a learning system according to a second embodiment. [Figure 26]FIG. 10 is a block diagram showing an example of the configuration of a learning device included in a learning system according to a second embodiment. [Figure 27] FIG. 10 is a conceptual diagram for explaining an example of learning by a learning device included in a learning system according to a second embodiment. [Figure 28] FIG. 10 is a conceptual diagram for explaining an example of learning by a learning device included in a learning system according to a second embodiment. [Figure 29] FIG. 10 is a block diagram showing an example of the configuration of a muscle strength evaluation device according to a third embodiment. [Figure 30] FIG. 2 is a block diagram showing an example of a hardware configuration for executing control and processing in each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. However, the embodiments described below are limited in a manner that is technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In all drawings used to describe the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. Furthermore, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0016] (First embodiment) First, a muscle strength evaluation system according to a first embodiment will be described with reference to the drawings. The muscle strength evaluation system of this embodiment measures sensor data related to foot movement in response to a user's walking. The muscle strength evaluation system of this embodiment uses the measured sensor data to estimate the user's muscle strength.

[0017] In this embodiment, an example is given in which muscle strength is estimated according to the correlation between features included in a walking pattern (also referred to as gait) and muscles related to the risk of falling (also referred to as muscles to be evaluated). The muscle strength of the muscles to be evaluated can be evaluated based on the variability of gait parameters. In this embodiment, muscle strength is estimated based on five related items (also referred to as five items) related to gait. The five items relate to total muscle strength of the whole body (grip strength), dynamic balance, lower limb muscle strength, mobility, and static balance. These five items are correlated with muscle strength. Although the five items are somewhat correlated with each other, they can basically be considered independent. In this embodiment, an example is given in which muscle strength is estimated mainly based on all five items. Muscle strength can be estimated based on at least one of the five items. A decrease in muscle strength related to the five items is a factor that increases the risk of falling.

[0018] (composition) FIG. 1 is a block diagram showing an example of the configuration of a muscle strength evaluation system 1 according to this embodiment. The muscle strength evaluation system 1 includes a gait measurement device 10 and a muscle strength evaluation device 13. In this embodiment, an example will be described in which the gait measurement device 10 and the muscle strength evaluation device 13 are configured as separate pieces of hardware. For example, the gait measurement device 10 is attached to the footwear of a subject (user) whose muscle strength is to be estimated. For example, the functions of the muscle strength evaluation device 13 are installed in a mobile terminal carried by the subject (user). Below, the configurations of the gait measurement device 10 and the muscle strength evaluation device 13 will be described separately.

[0019] [Gait measurement device] 2 is a block diagram showing an example of the configuration of gait measurement device 10. Gait measurement device 10 has sensor 11 and feature amount data generation unit 12. In this embodiment, an example is given in which sensor 11 and feature amount data generation unit 12 are integrated. Sensor 11 and feature amount data generation unit 12 may also be provided as separate devices.

[0020] 2, the sensor 11 has an acceleration sensor 111 and an angular velocity sensor 112. Fig. 2 shows an example in which the acceleration sensor 111 and the angular velocity sensor 112 are included in the sensor 11. The sensor 11 may include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Description of sensors other than the acceleration sensor 111 and the angular velocity sensor 112 that may be included in the sensor 11 will be omitted.

[0021] The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures acceleration (also called spatial acceleration) as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the feature data generation unit 12. For example, a piezoelectric, piezo-resistive, or capacitive sensor can be used as the acceleration sensor 111. There is no limitation on the measurement method of the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

[0022] The angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. The angular velocity sensor 112 measures angular velocity (also called spatial angular velocity) as a physical quantity related to foot movement. The angular velocity sensor 112 outputs the measured angular velocity to the feature data generation unit 12. For example, a vibration type or capacitance type sensor can be used as the angular velocity sensor 112. There is no limitation on the measurement method of the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0023] 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.

[0024] FIG. 3 is a conceptual diagram showing an example in which gait measurement device 10 is placed inside shoe 100 of a right foot. In the example of FIG. 3, gait measurement device 10 is installed at a position corresponding to the back of the arch of the foot. For example, gait measurement device 10 is placed in an insole inserted into shoe 100. For example, gait measurement device 10 may be placed on the bottom of shoe 100. For example, gait measurement device 10 may be embedded in the body of shoe 100. gait measurement device 10 may or may not be detachable from shoe 100. gait measurement device 10 may be installed at a position other than the back of the arch of the foot as long as it can measure sensor data related to foot movement. gait measurement device 10 may also be installed in socks worn by the user or in an accessory such as an anklet worn by the user. gait measurement device 10 may also be attached directly to the foot or embedded in the foot. 3 shows an example in which the gait measurement device 10 is installed in the right shoe 100. The gait measurement device 10 may also be installed in the shoes 100 on both feet.

[0025] In the example of FIG. 3 , a local coordinate system is set with the gait measurement device 10 (sensor 11) as the reference, and includes an x-axis in the left-right direction, a y-axis in the front-back direction, and a z-axis in the up-down direction. The x-axis is positive to the left, the y-axis is positive backward, and the z-axis is positive upward. The directions of the axes set in the sensor 11 may be the same for the left and right feet, or may be different for the left and right feet. For example, when sensors 11 manufactured to the same specifications are placed in left and right shoes 100, the up-down directions (directions of the Z-axis) of the sensors 11 placed in the left and right shoes 100 are the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right feet.

[0026] FIG. 4 is a conceptual diagram illustrating a local coordinate system (x-axis, y-axis, z-axis) set in gait measurement device 10 (sensor 11) installed on the backside of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set with respect to the ground. In the world coordinate system (x-axis, y-axis, z-axis), when a user is standing upright and facing the direction of travel, the user's sideways direction is set as the x-axis direction (positive for left), the direction of the user's back is set as the y-axis direction (positive for backward), and the direction of gravity is set as the z-axis direction (positive for vertical upward). Note that the example in FIG. 4 conceptually illustrates the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis), and does not accurately illustrate the relationship between the local coordinate system and the world coordinate system, which changes according to the user's walking.

[0027] FIG. 5 is a conceptual diagram illustrating planes (also called human body planes) set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. As shown in FIG. 5, when the user is standing upright with the center line of the feet facing the direction of travel, the world coordinate system and the local coordinate system coincide. In this embodiment, a rotation in the sagittal plane about the x-axis as the rotation axis is defined as roll, a rotation in the coronal plane about the y-axis as the rotation axis is defined as pitch, and a rotation in the horizontal plane about the z-axis as the rotation axis is defined as yaw. Furthermore, a rotation angle in the sagittal plane about the x-axis as the rotation axis is defined as roll angle, a rotation angle in the coronal plane about the y-axis as the rotation axis is defined as pitch angle, and a rotation angle in the horizontal plane about the z-axis as the rotation axis is defined as yaw angle.

[0028] As shown in FIG. 2 , feature data generation unit 12 (also referred to as a feature data generation device) includes an acquisition unit 121, a normalization unit 122, an extraction unit 123, a generation unit 125, and a feature data output unit 127. For example, feature data generation unit 12 is implemented by a microcomputer or microcontroller that performs overall control of gait measurement device 10 and data processing. For example, feature data generation unit 12 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc. The feature data generation unit 12 controls an acceleration sensor 111 and an angular velocity sensor 112 to measure angular velocity and acceleration. For example, feature data generation unit 12 may be implemented on the side of a mobile terminal (not shown) carried by the subject (user).

[0029] The acquiring unit 121 acquires acceleration in three axial directions from the acceleration sensor 111. The acquiring unit 121 also acquires angular velocities around three axes from the angular velocity sensor 112. For example, the acquiring unit 121 performs analog-to-digital conversion (AD conversion) on physical quantities (analog data) such as the acquired angular velocities and accelerations. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data by the acceleration sensor 111 and the angular velocity sensor 112, respectively. The acquiring unit 121 outputs the converted digital data (also referred to as sensor data) to the normalizing unit 122. The acquiring unit 121 may be configured to store the sensor data in a storage unit (not shown). The sensor data includes at least acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in the three axial directions. The angular velocity data includes angular velocity vectors around three axes. The acceleration data and angular velocity data are associated with the time when the data was acquired. The acquiring unit 121 may also apply corrections to the acceleration data and angular velocity data, such as correction for mounting error, temperature correction, and linearity correction.

[0030] The normalization unit 122 acquires sensor data from the acquisition unit 121. The normalization unit 122 extracts time series data for one walking cycle (also referred to as walking waveform data) from the time series data of acceleration in three axial directions and 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 walking cycle to a walking cycle of 0 to 100% (percent) (also referred to as first normalization). Timings such as 1% and 10% included in the 0 to 100% walking cycle are also referred to as walking phases. The normalization unit 122 also normalizes the first normalized walking waveform data for one walking cycle so that the stance phase is 60% and the swing phase is 40% (also referred to as second normalization). The stance phase is a period when at least a part of the sole of the foot is in contact with the ground. The swing phase is a period when the sole of the foot is off the ground. By subjecting the walking waveform data to second normalization, it is possible to suppress fluctuations in the walking phase from which feature values ​​are extracted due to the influence of disturbances.

[0031] FIG. 6 is a conceptual diagram illustrating a step cycle based on the right foot. The step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 6 represents one step cycle of the right foot, starting from the point when the heel of the right foot hits the ground and ending from the point when the heel of the right foot hits the ground again. The horizontal axis of FIG. 6 is first normalized so that the step cycle is 100%. The horizontal axis of FIG. 6 is also second normalized so that the stance phase is 60% and the swing phase is 40%. One step cycle of one leg is roughly divided into a stance phase in which at least a portion of the sole of the foot is in contact with the ground, and a swing phase in which the sole of the foot is off the ground. The stance phase is further divided into a load response period T1, a mid-stance period T2, a final stance period T3, and an early swing period T4. The swing phase is further divided into an early swing period T5, a mid-swing period T6, and a final swing period T7. Note that FIG. 6 is an example and does not limit the periods that make up a walking cycle or the names of these periods.

[0032] As shown in Figure 6, multiple events (also called walking events) occur during walking. P1 represents the event in which the heel of the right foot touches the ground (heel contact: HC). P2 represents the event in which the toe of the left foot leaves the ground (opposite toe off: OTO) while the sole of the right foot is in contact with the ground. P3 represents the event in which the heel of the right foot rises (heel rise: HR) while the sole of the right foot is in contact with the ground (opposite heel strike: OHS). P5 represents the event in which the toe of the right foot leaves the ground (toe off: TO) while the sole of the left foot is in contact with the ground (toe off: TO). P6 represents the event in which the left and right feet cross (foot crossing: FA) while the sole of the left foot is in contact with the ground (foot adjacent). P7 represents the event where the tibia of the right foot is nearly perpendicular to the ground (Tibia Vertical: TV) with the sole of the left foot touching the ground. P8 represents the event where the heel of the right foot touches the ground (Heel Contact: HC). P8 corresponds to the end point of the walking cycle that begins with P1 and also corresponds to the starting point of the next walking cycle. Note that Figure 6 is an example and does not limit the events that occur during walking or the names of those events.

[0033] FIG. 7 is a conceptual diagram for explaining an example of gait parameters. R , left foot step length S L , stride length T, step width W, foot angle F, and rotational distance DI are shown. Also, FIG. 7 shows the axis of travel PA, which is parallel to the axis of travel (Y-axis) and corresponds to the trajectory connecting the midpoints of the left and right feet. Right foot step length S R is the difference in Y coordinate between the heel of the right foot and the heel of the left foot when the state transitions from the state where the sole of the left foot is on the ground to the state where the heel of the right foot, which is swung in the direction of travel, is on the ground. Left foot step length S L is the difference in Y coordinate between the heel of the left foot and the heel of the right foot when the state transitions from the state where the sole of the right foot is on the ground to the state where the heel of the left foot, which is swung in the direction of travel, lands on the ground. The stride length T is the difference in Y coordinate between the right foot step length S R and left foot step length S LThe step width W is the distance between the right foot and the left foot. In FIG. 7, the step width W is the difference between the center line (X coordinate) of the heel of the right foot when in contact with the ground and the center line (X coordinate) of the heel of the left foot when in contact with the ground. The foot angle F is the angle formed between the center line of the foot and the direction of travel (Y axis) when the sole of the foot is in contact with the ground. In this embodiment, the foot angle is evaluated when the foot is in contact with the ground during the stance phase. The amount of rotation DI is the distance between the traveling axis PA and the foot at the time when the center axis of the foot is farthest from the traveling axis PA during the swing phase. In this embodiment, the amount of rotation DI is normalized by height because it is affected by the length of the lower limbs.

[0034] FIG. 8 is a diagram illustrating an example of detecting heel strike HC and toe lift TO from time series data (solid line) of forward acceleration (Y-direction acceleration). The timing of heel strike HC is the timing of the minimum peak immediately after the maximum peak that appears in the time series data of forward acceleration (Y-direction acceleration). The maximum peak that marks the timing of heel strike HC corresponds to the maximum peak of the gait waveform data for one step cycle. The section between consecutive heel strikes HC is one step cycle. The timing of toe lift TO is the timing of the rise of the maximum peak that appears after the stance phase period during which no fluctuations appear in the time series data of forward acceleration (Y-direction acceleration). FIG. 8 also shows time series data (dashed line) of roll angle (angular velocity about the X-axis). The midpoint between the timing of the minimum roll angle and the timing of the maximum roll angle corresponds to the mid-stance phase. For example, parameters such as walking speed, stride length, circumduction, internal rotation / external rotation, and plantar flexion / dorsiflexion (also called gait parameters) can be determined using the mid-stance phase as a reference.

[0035] FIG. 9 is a diagram illustrating an example of gait waveform data normalized by the normalization unit 122. The normalization unit 122 detects heel strikes HC and toe lifts TO from time-series data of forward acceleration (Y-direction acceleration). The normalization unit 122 extracts the interval between successive heel strikes HC as gait waveform data for one step cycle. The normalization unit 122 converts the horizontal axis (time axis) of the gait waveform data for one step cycle into a gait cycle of 0 to 100% by first normalization. In FIG. 9, the gait waveform data after the first normalization is shown by a dashed line. In the gait waveform data (dashed line) after the first normalization, the timing of toe lifts TO is shifted from 60%.

[0036] In the example of FIG. 9, the normalization unit 122 normalizes the section from heel-strike HC, where the walking phase is 0%, to toe-off TO, which follows the heel-strike HC, to 0-60%. The normalization unit 122 also normalizes the section from toe-off TO to heel-strike HC, where the walking phase is 100%, to 60-100%. As a result, the gait waveform data for one step cycle is normalized into a section where the gait cycle is 0-60% (stance phase) and a section where the gait cycle is 60-100% (swing phase). In FIG. 9, the gait waveform data after the second normalization is shown by the solid line. In the gait waveform data after the second normalization (solid line), the timing of toe-off TO coincides with 60%.

[0037] 8 and 9 show an example in which gait waveform data for one step cycle is extracted and normalized based on the traveling acceleration (Y-direction acceleration). With respect to accelerations / angular velocities other than the traveling acceleration (Y-direction acceleration), the normalization unit 122 extracts and normalizes gait waveform data for one step cycle in accordance with the traveling acceleration (Y-direction acceleration) gait cycle. The normalization unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In this case, the normalization unit 122 extracts and normalizes gait waveform data for one step cycle in accordance with the traveling acceleration (Y-direction acceleration) gait cycle, also with respect to angles around three axes.

[0038] The normalization unit 122 may extract / normalize the gait waveform data for one step gait cycle based on acceleration / angular velocity other than the forward acceleration (Y-direction acceleration) (not shown in the drawings). For example, the normalization unit 122 may detect heel strike HC and toe lift TO from time series data of vertical acceleration (Z-direction acceleration). The timing of heel strike HC is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of vertical acceleration (Z-direction acceleration) becomes almost zero. The minimum peak that marks the timing of heel strike HC corresponds to the minimum peak of the gait waveform data for one step gait cycle. The section between consecutive heel strikes HC is one step gait cycle. The timing of toe-off TO is the timing of an inflection point in the time-series data of vertical acceleration (Z-direction acceleration) where the vertical acceleration (Z-direction acceleration) gradually increases after passing through a section of small fluctuations following the maximum peak immediately after heel-contact HC. The normalization unit 122 may also extract / normalize the gait waveform data for one walking cycle based on both the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration). The normalization unit 122 may also extract / normalize the gait waveform data for one walking cycle based on acceleration, angular velocity, angle, etc. other than the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration).

[0039] The extraction unit 123 acquires walking waveform data for one step gait cycle normalized by the normalization unit 122. The extraction unit 123 extracts feature amounts used for estimating muscle strength from the walking waveform data for one step gait cycle. The extraction unit 123 extracts feature amounts for each walking phase cluster from walking phase clusters that integrate temporally consecutive walking phases based on preset conditions. A walking phase cluster includes at least one walking phase. A walking phase cluster may also include a single walking phase. The walking waveform data and walking phases from which feature amounts used for estimating muscle strength are extracted will be described later.

[0040] FIG. 10 is a conceptual diagram illustrating the extraction of feature quantities for estimating muscle force from walking waveform data for one step cycle. For example, the extraction unit 123 extracts temporally consecutive walking phases i to i+m as a walking phase cluster CL (i and m are natural numbers). The walking phase cluster CL includes m walking phases (components). That is, the number of walking phases (components) constituting the walking phase cluster CL (also referred to as the number of components) is m. While FIG. 10 illustrates an example in which the walking phases are integer values, the walking phases may be subdivided to the nearest decimal point. When the walking phases are subdivided to the nearest decimal point, the number of components of the walking phase cluster CL corresponds to the number of data points in the section of the walking phase cluster. The extraction unit 123 extracts feature quantities from each of the walking phases i to i+m. When the walking phase cluster CL is composed of a single walking phase j, the extraction unit 123 extracts feature quantities from the single walking phase j (j is a natural number).

[0041] The generation unit 125 applies a feature composition formula to a feature (first feature) extracted from each of the walking phases constituting the walking phase cluster to generate a feature (second feature) of the walking phase cluster. The feature composition formula is a calculation formula set in advance for generating a feature of the walking phase cluster. For example, the feature composition formula is a calculation formula related to four arithmetic operations. For example, the second feature calculated using the feature composition formula is an integral average value, arithmetic mean value, slope, variance, etc. of the first feature in each walking phase included in the walking phase cluster. For example, the generation unit 125 applies, as the feature composition formula, a calculation formula for calculating the slope or variance of the first feature extracted from each of the walking phases constituting the walking phase cluster. For example, when a walking phase cluster is composed of a single walking phase, it is not possible to calculate the slope or variance, so a feature composition formula for calculating an integral average value, arithmetic mean value, etc. may be used.

[0042] 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 muscle strength evaluation device 13, which uses the feature data.

[0043] [Muscle strength evaluation device] 11 is a block diagram showing an example of the configuration of the muscle strength evaluation device 13. The muscle strength evaluation device 13 has a data acquisition unit 131, a memory unit 132, a physical ability estimation unit 133, a muscle strength evaluation unit 134, and an output unit 135. The physical ability estimation unit 133 and the muscle strength evaluation unit 134 constitute the evaluation unit 130.

[0044] Data acquisition unit 131 acquires feature amount data from gait measurement device 10. Data acquisition unit 131 outputs the received feature amount data to physical ability estimation unit 133. Data acquisition unit 131 may receive the feature amount data from gait measurement device 10 via a wired connection such as a cable, or may receive the feature amount data from gait measurement device 10 via wireless communication. For example, data acquisition unit 131 is configured to receive the feature amount data from gait measurement device 10 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of data acquisition unit 131 may be compliant with standards other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0045] The storage unit 132 stores an estimation model that estimates the muscle strength of a muscle to be evaluated using feature amount data extracted from the walking waveform data. The storage unit 132 stores estimation models that estimate the muscle strength of a muscle to be evaluated, which have been trained on a plurality of subjects. For example, the storage unit 132 stores an estimation model that outputs a muscle strength index (also called a muscle strength score) of a muscle to be evaluated in response to input of feature amount data extracted from the walking waveform data.

[0046] 12 is a conceptual diagram for explaining five related items (also referred to as five items) related to the muscle strength of the muscle to be evaluated. The muscle strength of the muscle to be evaluated is related to five items: total muscle strength of the whole body (grip strength), dynamic balance, lower limb muscle strength, mobility, and static balance. Details of the five items related to the muscle strength of the muscle to be evaluated, total muscle strength of the whole body (grip strength), dynamic balance, lower limb muscle strength, mobility, and static balance, will be described later.

[0047] For example, the storage unit 132 stores an estimation model that outputs a muscle strength index (muscle strength score) for a muscle to be evaluated in response to input of feature amount data common to the estimation of the muscle strength of the muscle. For example, the storage unit 132 stores an estimation model (also referred to as a physical ability estimation model) that outputs a score for each of five items in response to input of feature amount data used to estimate the score for each of the five items. For example, the storage unit 132 stores an estimation model (also referred to as a muscle strength estimation model) that outputs a muscle strength index (muscle strength score) for a muscle to be evaluated in response to input of the scores for the five items.

[0048] The estimation model may be stored in the storage unit 132 at the time of product shipment from the factory or at the time of calibration before the muscle strength evaluation system 1 is used by a user. For example, the estimation model may be stored in a storage device such as an external server. In this case, the estimation model may be used via an interface (not shown) connected to the storage device.

[0049] The physical ability estimation unit 133 acquires feature amount data from the data acquisition unit 131. The physical ability estimation unit 133 estimates the user's physical ability using the acquired feature amount data. The physical ability estimation unit 133 inputs the feature amount data to a physical ability estimation model stored in the storage unit 132. The physical ability estimation unit 133 estimates the user's physical ability using a physical ability index (score) corresponding to the physical ability output from the physical ability estimation model. For example, the physical ability estimation unit 133 estimates the user's physical ability using each score of five items in response to input of feature amount data used to estimate each score of the five items. The physical ability estimation unit 133 outputs an estimation result using the physical ability index (score) output from the physical ability estimation model to the muscle strength evaluation unit 134. For example, the physical ability estimation unit 133 outputs the physical ability index (score) output from the physical ability estimation model in response to input of feature amount data to the muscle strength evaluation unit 134.

[0050] The muscle strength evaluation unit 134 acquires, from the physical ability estimation unit 133, the estimation result of the physical ability estimated by the physical ability estimation unit 133 using the feature amount data. The muscle strength evaluation unit 134 estimates the muscle strength of the muscle to be evaluated using the acquired estimation result. For example, the muscle strength evaluation unit 134 acquires a physical ability index (score) output from the physical ability estimation model in response to input of the feature amount data. The muscle strength evaluation unit 134 inputs the physical ability index (score) to the muscle strength estimation model stored in the memory unit 132. The muscle strength evaluation unit 134 evaluates the muscle strength of the muscle to be evaluated of the user using the muscle strength index (score) of the muscle to be evaluated output from the muscle strength estimation model in response to input of the scores for each of the five items. For example, the muscle strength evaluation unit 134 outputs to the output unit 135 the muscle strength index (score) of the muscle to be evaluated output from the muscle strength estimation model in response to input of the scores for each of the five items.

[0051] The physical ability estimation unit 133 and the muscle strength evaluation unit 134 may use an estimation model stored in an external storage device constructed on a cloud, a server, etc. In this case, the physical ability estimation unit 133 and the muscle strength evaluation unit 134 are configured to use the physical ability estimation model via an interface (not shown) connected to the storage device.

[0052] The output unit 135 outputs the evaluation result of the muscle strength of the muscle to be evaluated by the muscle strength evaluation unit 134. For example, the output unit 135 displays the evaluation result regarding the muscle strength of the muscle to be evaluated on the screen of the subject (user)'s mobile terminal. For example, the output unit 135 outputs the evaluation result to an external system that uses the evaluation result. There are no particular limitations on how the evaluation result output from the muscle strength evaluation device 13 can be used.

[0053] For example, the muscle strength evaluation device 13 is connected to an external system, such as a cloud or a server, via 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 portable communication device with a communication function, such as a smartphone, a smart watch, or a mobile phone. For example, the muscle strength evaluation device 13 is connected to the mobile terminal via a wired connection, such as a cable. For example, the muscle strength evaluation device 13 is connected to the mobile terminal via wireless communication. For example, the muscle strength evaluation device 13 is connected to the mobile terminal via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the muscle strength evaluation device 13 may also conform to standards other than Bluetooth (registered trademark) or WiFi (registered trademark). The evaluation results of the muscle strength of the evaluation target muscle may be used by an application installed on the mobile terminal. In this case, the mobile terminal executes processing using the evaluation results using application software or the like installed on the mobile terminal.

[0054] Next, we will explain each of the five items related to the muscles to be evaluated shown in Figure 12: total muscle strength (grip strength), dynamic balance, lower limb muscle strength, mobility, and static balance. After explaining each of the five items, we will explain the feature values ​​used to estimate the muscle strength of the muscles to be evaluated. Muscle strength is estimated using feature values ​​common to the five items.

[0055] <Related item 1> Related item 1 relates to total muscle strength of the whole body. There is a correlation between total muscle strength and grip strength. Grip strength is also correlated with knee extension strength. One indicator of total muscle strength related to related item 1 is grip strength. For example, an estimated value of grip strength is an indicator of total muscle strength. For example, a score based on the estimated value of grip strength (also called a total muscle strength score) is an indicator of total muscle strength. The total muscle strength score is a value obtained by scoring grip strength, which is an indicator of total muscle strength, using a predetermined standard. Grip strength is affected by attributes such as gender, age, and height. Therefore, the total muscle strength score may be scored using a standard for each attribute. In particular, grip strength is affected by gender. Therefore, the total muscle strength score may be scored using different standards depending on gender. Note that the indicator of total muscle strength is not limited to grip strength, as long as it is possible to score total muscle strength.

[0056] FIG. 13 is a correspondence table summarizing the feature values ​​used to estimate total muscle strength (grip strength). The correspondence table in FIG. 13 associates the feature value number with the gait waveform data from which the feature value is extracted, the gait phase (%) from which the gait phase cluster is extracted, and the associated muscles. The gait phase from which the feature values ​​used to estimate grip strength are extracted differs depending on gender. For men, there is a correlation between quadriceps activity and grip strength. Therefore, to estimate men's grip strength, feature values ​​AM1 to AM4 extracted from gait phases in which the characteristics of quadriceps activity are apparent are used. For women, there is a correlation between grip strength and the activities of the quadriceps muscles, vastus lateralis, vastus intermedius, and vastus medialis. Therefore, to estimate women's grip strength, feature values ​​AF1 to AF3 extracted from gait phases in which the characteristics of vastus lateralis, vastus intermedius, and vastus medialis are apparent are used.

[0057] The feature AM1 is extracted from the 3% walking phase of the walking waveform data Ay, which is related to the time-series data of forward acceleration (Y-direction acceleration). The 3% walking phase is included in the load response period T1. The feature AM1 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis muscles of the quadriceps femoris.

[0058] Feature AM2 is extracted from the section of the walking phase 59-62% of the walking waveform data Ay related to the time-series data of forward acceleration (Y-direction acceleration). The walking phase 59-62% is included in the early swing phase T4. Feature AM2 mainly includes features related to the movement of the rectus femoris muscle, which is one of the quadriceps muscles.

[0059] Feature AM3 is extracted from the 59% to 62% walking phase of the walking waveform data Az, which is related to the time-series data of vertical acceleration (Z-direction acceleration). The 59% to 62% walking phase is included in the early swing phase T4. Feature AM3 mainly includes features related to the movement of the rectus femoris, which is one of the quadriceps muscles.

[0060] Feature AM4 is the ratio of the period from heel-contact to toe-off of the opposite foot to the period when both feet are simultaneously on the ground (DST1: Double Support Time). DST1 is the ratio of the period from heel-contact to toe-off of the opposite foot in a gait cycle. Feature AM4 mainly includes features attributable to the quadriceps femoris.

[0061] The feature AF1 is extracted from the 13% section of the walking phase of the walking waveform data Ax, which is related to the time-series data of lateral acceleration (X-direction acceleration). The 13% walking phase is included in the mid-stance phase T2. The feature AF1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis muscles of the quadriceps femoris.

[0062] The feature AF2 is extracted from the 7-10% section of the walking phase of the walking waveform data Gy, which is related to the time series data of angular velocity (pitch angular velocity) in the coronal plane (around the Y-axis). The 7-10% walking phase is included in the load response period T1. The feature AF2 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis muscles.

[0063] Feature AF3 is the proportion of the period from heel contact to toe-off of the opposite foot (DST2) to the period during which both feet are simultaneously in contact with the ground (DST: Double Support Time). DST2 is the proportion of the period during a gait cycle from heel contact to toe-off of the opposite foot. The sum of DST1 and DST2 corresponds to the period during which both feet are simultaneously in contact with the ground during a gait cycle. Feature AF3 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis.

[0064] <Related item 2> Related item 2 relates to dynamic balance. Dynamic balance can be evaluated by the performance of the Functional Reach Test (FRT). In this embodiment, the performance of the FRT is evaluated based on the distance between the fingertips (also referred to as the functional reach distance) when the upper limbs are moved as far forward as possible from a standing position with both hands raised 90 degrees relative to the horizontal. The functional reach distance (hereinafter referred to as the FR distance) is the performance value of the FRT. The larger the FR distance, the higher the performance of the FRT. Related item 2 may also be evaluated using a method other than the FRT performed with both hands. For example, related item 2 may be evaluated based on the performance of the FRT performed with one hand or other variations of the FRT.

[0065] The dynamic balance index for related item 2 is the FR distance. For example, an estimated value of the FR distance is the dynamic balance index. For example, a score according to the estimated value of the FR distance (also called the dynamic balance score) is the dynamic balance index. The dynamic balance score is a value obtained by scoring the FR distance, which is an index of dynamic balance, based on a preset standard. Dynamic balance is affected by attributes such as height. Therefore, the dynamic balance score may be scored based on a standard for each attribute. Note that the dynamic balance index is not limited to the FR distance as long as it is possible to score dynamic balance.

[0066] FIG. 14 is a correspondence table summarizing the feature quantities used in estimating dynamic balance. The correspondence table in FIG. 14 associates the feature quantity number, the gait waveform data from which the feature quantity is extracted, the gait phase (%) from which the gait phase cluster is extracted, and the associated muscles. The FR distance is correlated with the activity of the gluteus medius, iliacus, hamstrings (long head of biceps femoris), tibialis anterior, etc., as well as the magnitude of the compensatory movement of turning the toes outward. Therefore, feature quantities B1 to B5 extracted from the gait phases in which these features appear are used to estimate the FR distance.

[0067] Feature B1 is extracted from the 75-79% gait phase of the gait waveform data Ay, which is related to the time-series data of forward acceleration (Y-direction acceleration). The 75-79% gait phase is included in the mid-swing phase T6. Feature B1 mainly includes features related to the movement of the tibialis anterior muscle and the short head of the biceps femoris muscle.

[0068] Feature B2 is extracted from the 62% walking phase of the walking waveform data Az, which is related to the time-series data of vertical acceleration (Z-direction acceleration). The 62% walking phase is included in the initial swing phase T5. Feature B2 mainly includes features related to the movement of the iliacus muscle.

[0069] Feature B3 is extracted from the 7-8% section of the walking phase of the walking waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 7-8% walking phase is included in the load response period T1. Feature B3 mainly includes features related to the movement of the gluteus medius muscle.

[0070] Feature B4 is extracted from the 57-58% section of the walking phase of the walking waveform data Ez, which is related to time-series data of angles (postural angles) in the horizontal plane (around the Z axis). The 57-58% walking phase is included in the early swing phase T4. Feature B4 mainly includes features related to compensatory movements. Compensatory movements are movements that change the foot angle to gain stability in order to compensate for the decline in balance ability and muscle function that occurs with aging.

[0071] The feature quantity B5 is the average value of the foot angle in the horizontal plane during the swing phase. For example, the feature quantity B5 is the average value of the gait waveform data Ez during the swing phase. In other words, the feature quantity B5 is the integral value of the gait waveform data Gz related to the time-series data of angular velocity in the horizontal plane (around the Z-axis). The feature quantity B5 mainly includes features related to compensatory movements.

[0072] <Related item 3> Related item 3 relates to lower limb muscle strength. Lower limb muscle strength can be evaluated by the results of a chair stand test. In this embodiment, the results of the 5-chair stand test, in which the subject stands up and sits down from a chair five times, are evaluated. The 5-chair stand test is also called the SS-5 (Sit to Stand-5) test. The results of the 5-chair stand test are evaluated based on the time it takes to stand up and sit down from a chair five times (also called the sit-to-stand time). The sit-to-stand time is the score value of the SS-5 test. The shorter the sit-to-stand time, the higher the score of the SS-5 test. Lower limb muscle strength may also be evaluated by the results of a 30-second chair stand (CS-30) test, which measures the number of times the subject stands up and sits down from a chair in 30 seconds.

[0073] An index of lower limb muscle strength related to related item 3 is the standing-sitting time. For example, an estimated value of the standing-sitting time five times is an index of lower limb muscle strength. For example, a score according to the estimated value of the standing-sitting time (also called a lower limb muscle strength score) is an index of lower limb muscle strength. The lower limb muscle strength score is a value obtained by scoring the standing-sitting time, which is an index of lower limb muscle strength, using a preset standard. Lower limb muscle strength is affected by attributes such as age. Therefore, the lower limb muscle strength score may be scored using a standard for each attribute. Note that the index of lower limb muscle strength is not limited to the standing-sitting time, as long as lower limb muscle strength can be scored.

[0074] FIG. 15 is a correspondence table summarizing the feature values ​​used to estimate lower limb muscle strength. The correspondence table in FIG. 15 associates the feature value number, the gait waveform data from which the feature value is extracted, the gait phase (%) from which the gait phase cluster is extracted, and the associated muscles. The standing-sitting time is correlated with the quadriceps, hamstrings, tibialis anterior, and gastrocnemius. Therefore, feature values ​​C1 to C4 extracted from the gait phases in which these features appear are used to estimate the standing-sitting time.

[0075] The feature C1 is extracted from the section of the walking phase 42 to 54% of the walking waveform data Gx related to the time series data of angular velocity in the sagittal plane (around the X axis). The walking phase 42 to 54% is the section from the end of stance phase T3 to the early swing phase T4. The feature C1 mainly includes features related to the movement of the gastrocnemius muscles.

[0076] Feature C2 is extracted from the 99% to 100% gait phase section of the walking waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 99% to 100% gait phase corresponds to the end of the swing phase T7. Feature C2 mainly includes features related to the movements of the quadriceps, hamstrings, and tibialis anterior.

[0077] Feature C3 is extracted from the gait phase 10-12% section of the gait waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y axis). The gait phase 10-12% corresponds to the beginning of mid-stance phase T2. Feature C3 mainly includes features related to the movements of the quadriceps, hamstrings, and gastrocnemius muscles.

[0078] Feature C4 is extracted from the 99% walking phase section of the walking waveform data Ez, which is related to time-series data of angles (postural angles) in the horizontal plane (around the Z axis). The 99% walking phase corresponds to the end of the swing phase T7. Feature C4 mainly includes features related to the movements of the quadriceps, hamstrings, and tibialis anterior.

[0079] <Related item 4> Related item 4 relates to mobility. Mobility can be evaluated by the results of a TUG (Time Up and Go) test. In this embodiment, the results of the TUG test are evaluated based on the time it takes to stand up from a chair, walk to a landmark 3 meters away, turn around, and sit back down in the chair (also referred to as the TUG time). The TUG time is the score value of the TUG test. The shorter the TUG time, the higher the score on the TUG test. Related item 4 may also be evaluated by the results of a test related to mobility other than the TUG test.

[0080] The mobility index for related item 4 is the time required to complete the TUG. For example, an estimated value of the time required to complete the TUG is an index of mobility. For example, a score (also called a mobility score) according to the estimated value of the time required to complete the TUG is an index of mobility. The mobility score is a value obtained by scoring the time required to complete the TUG, which is an index of mobility, based on a preset standard. Mobility is affected by attributes such as age. Therefore, the mobility score may be scored based on a standard for each attribute. Note that the mobility index is not limited to the time required to complete the TUG, as long as mobility can be scored.

[0081] Figure 16 is a correspondence table summarizing the features used to estimate mobility. The correspondence table in Figure 16 associates feature numbers, gait waveform data from which the features are extracted, gait phases (%) from which gait phase clusters are extracted, and related muscles. The TUG duration is correlated with the quadriceps, gluteus medius, and tibialis anterior muscles. Therefore, feature values ​​D1 to D6 extracted from gait phases in which these features appear are used to estimate the TUG duration. The characteristics of the tensor fasciae latae muscle appear in gait phases 0 to 45% and 85 to 100%. The characteristics of the gluteus medius muscle appear in gait phases 0 to 25%. The characteristics of the tibialis anterior muscle appear in gait phases 0 to 10% and 57 to 100%.

[0082] The feature value D1 is extracted from the section of the walking phase 64 to 65% of the walking waveform data Ax related to the time-series data of the lateral acceleration (X-direction acceleration). The walking phase 64 to 65% is included in the initial swing phase T5. The feature value D1 mainly includes features related to the movement of the quadriceps femoris during the standing-to-sitting motion.

[0083] Feature D2 is extracted from the 57% to 58% walking phase of the walking waveform data Gx, which is related to time-series data of angular velocity in the sagittal plane (around the X-axis). The 57% to 58% walking phase is included in the early swing phase T4. Feature D2 mainly includes features related to the movement of the quadriceps, which is related to the kick-off speed of the foot.

[0084] Feature D3 is extracted from the gait phase 19-20% section of the gait waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y axis). The gait phase 19-20% is included in the mid-stance phase T2. Feature D3 mainly includes features related to the movement of the gluteus medius muscle during changes of direction.

[0085] Feature D4 is extracted from the gait phase 12-13% section of the gait waveform data Ez, which is related to time-series data of angular velocity in the horizontal plane (around the Z axis). The gait phase 12-13% corresponds to the beginning of mid-stance phase T2. Feature D4 mainly includes features related to the movement of the gluteus medius muscle during changes of direction.

[0086] Feature D5 is extracted from the gait phase 74-75% section of the walking waveform data Ez, which is related to the time series data of angular velocity in the horizontal plane (around the Z axis). The gait phase 74-75% corresponds to the beginning of the mid-swing phase T6. Feature D5 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction.

[0087] Feature D6 is extracted from the 76% to 80% gait phase of the gait waveform data Ey, which is related to the time series data of angles (postural angles) in the coronal plane (around the Y-axis). The 76% to 80% gait phase is included in the mid-swing phase T6. Feature D6 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction.

[0088] <Related item 5> Related item 5 relates to static balance. Static balance can be evaluated by the performance of a one-leg standing test. In this embodiment, the performance of the one-leg standing test is evaluated based on the time spent with one leg raised 5 cm (centimeters) from the ground with the eyes closed (also referred to as the one-leg standing time). The one-leg standing time is a static balance performance value. The longer the one-leg standing time, the higher the static balance performance. Related item 5 may also be evaluated by performance other than the one-leg standing test with eyes closed. For example, related item 5 may be evaluated by a one-leg standing test with the eyes open (one-leg standing test with eyes open) or other variations of the one-leg standing test.

[0089] The static balance index for related item 5 is the one-leg standing time. For example, an estimated value of the one-leg standing time is an index of static balance. For example, a score (also called a static balance score) according to the estimated value of the one-leg standing time is an index of static balance. The static balance score is a value obtained by scoring the one-leg standing time, which is an index of static balance, based on a preset standard. Static balance is affected by attributes such as age and height. Therefore, the static balance score may be scored based on a standard for each attribute. Note that the index of static balance is not limited to the one-leg standing time, as long as static balance can be scored.

[0090] FIG. 17 is a correspondence table summarizing the feature quantities used in estimating static balance. The correspondence table in FIG. 17 associates the feature quantity number, the gait waveform data from which the feature quantity is extracted, the gait phase (%) from which the gait phase cluster is extracted, and the related muscles. The single-leg standing time is correlated with the gluteus medius, adductor longus, sartorius, and abductor and adductor muscles. Therefore, feature quantities E1 to E7 extracted from the gait phases in which these features appear are used to estimate the single-leg standing time.

[0091] The feature E1 is extracted from the gait phase 13-19% section of the gait waveform data Ax related to the time series data of lateral acceleration (X-direction acceleration). The gait phase 13-19% is included in the mid-stance phase T2. The feature E1 mainly includes features related to the movement of the gluteus medius muscle.

[0092] The feature E2 is extracted from the 95% walking phase of the walking waveform data Az related to the time-series data of vertical acceleration (Z-direction acceleration). The 95% walking phase corresponds to the end of the swing phase T7. The feature E2 mainly includes features related to the movement of the gluteus medius muscle.

[0093] Feature E3 is extracted from the gait phase 64-65% section of the gait waveform data Gy, which is related to the time series data of angular velocity in the coronal plane (around the Y-axis). The gait phase 64-65% is included in the initial swing phase T5. Feature E3 mainly includes features related to the movement of the adductor longus and sartorius muscles.

[0094] Feature E4 is extracted from the gait phase 11-16% section of the gait waveform data Gz, which is related to the time series data of angular velocity in the horizontal plane (around the Z axis). The gait phase 11-16% is included in the mid-stance phase T2. Feature E4 mainly includes features related to the movement of the gluteus medius muscle.

[0095] Feature E5 is extracted from the 57-58% section of the walking phase of the walking waveform data Gz, which is related to the time series data of angular velocity in the horizontal plane (around the Z axis). The 57-58% walking phase is included in the early swing phase T4. Feature E5 mainly includes features related to the movement of the adductor longus and sartorius muscles.

[0096] The feature quantity E6 is extracted from the 100% walking phase section of the walking waveform data Ez, which is related to time-series data of angles (postural angles) in the horizontal plane (around the Z-axis). The 100% walking phase corresponds to the timing of heel contact, when the phase switches from the end-swing phase T7 to the load response phase T1. The feature quantity of the walking waveform data Ez in the 100% walking phase corresponds to the foot angle when the sole of the foot is in contact with the ground. The feature quantity E6 mainly includes features related to the movement of the gluteus medius muscle.

[0097] Feature E7 is the distance (minutes of rotation) between the axis of forward movement and the foot at the time when the central axis of the foot is farthest from the axis of forward movement during the swing phase. Feature E7 is the minute of rotation normalized by the subject's height. Feature E7 mainly includes features related to the movement of the abductor and adductor muscles.

[0098] FIG. 18 is a diagram summarizing an example of the relationship between five items related to fall risk and muscles. In FIG. 18, solid lines connect the five items related to fall risk and muscles. Note that the solid lines in FIG. 18 indicate a representative relationship. Items without solid lines do not indicate that there is no correlation between the items and muscles. Also, FIG. 18 includes not a single muscle but a collective name for several muscles. The muscles listed in FIG. 18 are examples of muscles to be evaluated related to fall risk. The muscles listed in FIG. 18 are not all of the muscles to be evaluated. The muscle strength evaluation unit 134 evaluates the activity of muscles related to the five items related to fall risk according to the relationship between the indices (scores) of those five items.

[0099] The quadriceps, tibialis anterior, and gluteus medius are involved in multiple areas: the quadriceps are involved in overall muscle strength, leg strength, and mobility; the tibialis anterior is involved in dynamic balance, leg strength, and mobility; and the gluteus medius is involved in dynamic balance, mobility, and static balance.

[0100] For example, if the scores for total muscle strength, lower limb muscle strength, and mobility are smaller than the reference values, the muscle strength evaluation unit 134 evaluates that the muscle strength of the quadriceps is reduced. For example, if the scores for dynamic balance, lower limb muscle strength, and mobility are smaller than the reference values, the muscle strength evaluation unit 134 evaluates that the muscle strength of the tibialis anterior is reduced. For example, if the scores for dynamic balance, mobility, and static balance are smaller than the reference values, the muscle strength evaluation unit 134 evaluates that the muscle strength of the gluteus medius is reduced.

[0101] The iliacus, short head of biceps femoris, hamstrings, gastrocnemius, abductor and adductor muscles, adductor longus, and sartorius are associated with individual items. The iliacus and short head of biceps femoris are associated with dynamic balance. The hamstrings and gastrocnemius are associated with lower limb strength. The abductor and adductor muscles, adductor longus, and sartorius are associated with static balance.

[0102] For example, if the dynamic balance score is lower than the reference value, the muscle strength evaluation unit 134 evaluates that the muscle strength of the iliacus muscle and the short head of the biceps femoris is reduced. For example, if the lower limb muscle strength score is lower than the reference value, the muscle strength evaluation unit 134 evaluates that the muscle strength of the hamstrings and gastrocnemius is reduced. For example, if the static balance score is lower than the reference value, the muscle strength evaluation unit 134 evaluates that the muscle strength of the abductor and adductor muscle group, adductor longus, and sartorius is reduced.

[0103] Fall risk can be assessed according to the scores of five items related to fall risk. For example, fall risk R can be calculated using the following formula 1: R=A×SA+B×SB+C×SC+D×SD+E×SE...(1) In the above formula 1, SA is the total muscle strength score of the whole body. SB is the dynamic balance score. SC is the lower limb muscle strength score. SD is the mobility score. SE is the static balance score. The total muscle strength score of the whole body S1, the dynamic balance score S2, the lower limb muscle strength score S3, the mobility score S4, and the static balance score S5 can be estimated using the estimation model corresponding to each score.

[0104] In the above formula 1, A to E are normalized weighting coefficients. The weighting coefficients A to E are set in advance based on known knowledge. For example, the weighting coefficients A to E can be determined based on the movements in a physical ability test corresponding to each weighting coefficient. For example, the weighting coefficients A to E can be determined based on the ratio of the EMG signal strength of the muscles to be evaluated in the physical ability test corresponding to each weighting coefficient. For example, for multiple subjects, EMG sensors are attached to measurement sites of predetermined muscles to be evaluated, and tests on five items related to fall risk are conducted. By conducting such tests, the weighting coefficients A to E can be set based on the ratio of the EMG signal strength of the muscles to be evaluated.

[0105] For example, the weighting coefficients A to E are determined by the weighting of the signal strength of the gait waveform data related to the gait cycle. When the scores for the five items related to the risk of falling are estimated from the gait signal, the activity of each muscle during the same gait cycle is calculated as a ratio and distributed to the coefficients.

[0106] The activity of the quadriceps is related to the total muscle strength of the whole body. The relative weight of the quadriceps in the total muscle strength score of the whole body S1 is defined as MA1.

[0107] Dynamic balance involves the tibialis anterior, gluteus medius, iliacus, and short head of the biceps femoris. The weight of the tibialis anterior in the dynamic balance score S2 is designated MB2. The weight of the gluteus medius in the dynamic balance score S2 is designated MB3. The weight of the iliacus in the dynamic balance score S2 is designated MB4. The weight of the short head of the biceps femoris in the dynamic balance score S2 is designated MB5.

[0108] Lower limb muscle strength involves the quadriceps, tibialis anterior, hamstrings, and gastrocnemius. The relative weight of the quadriceps in a lower limb muscle strength score of S3 is MC1. The relative weight of the tibialis anterior in a lower limb muscle strength score of S3 is MC2. The relative weight of the hamstrings in a lower limb muscle strength score of S3 is MC6. The relative weight of the gastrocnemius in a lower limb muscle strength score of S3 is MC7.

[0109] The quadriceps, tibialis anterior, and gluteus medius muscles are involved in mobility. The relative weight of the quadriceps in mobility score S4 is designated as MD1. The relative weight of the tibialis anterior in mobility score S4 is designated as MD2. The relative weight of the gluteus medius in mobility score S4 is designated as MD3.

[0110] Static balance involves the abductor and adductor muscles, adductor longus, and sartorius. The weight of the abductor and adductor muscles in the static balance score S5 is MD8. The weight of the adductor longus in the static balance score S5 is MD9. The weight of the sartorius in the static balance score S5 is MD10.

[0111] The muscle strength evaluation unit 134 calculates the muscle strength score of the muscle to be evaluated using the product of the specific gravity of the muscle to be evaluated, the specific gravity coefficient of the muscle to be evaluated, and the physical ability score related to the physical ability related to the muscle to be evaluated. Examples of calculations of the muscle strength score by the muscle strength evaluation unit 134 are listed below.

[0112] The quadriceps muscle strength score MS1 is calculated using Equation 2 below. MS1=MA1×A×SA+MC1×C×SC+MD1×D×SD...(2) The tibialis anterior muscle strength score MS2 is calculated using Equation 3 below. MS2=MB2×B×SB+MC2×C×SC+MD2×D×SD...(3) The gluteus medius muscle strength score MS3 is calculated using Equation 4 below. MS3=MB3×B×SB+MC3×C×SC+MD4×D×SD...(4) The iliacus muscle strength score MS4 is calculated using Equation 5 below. MS4 = MB4 × B × SB (5) The muscle strength score MS5 of the short head of the biceps femoris is calculated using Equation 6 below. MS5 = MB5 × B × SB (6) The hamstring strength score MS6 is calculated using Equation 7 below. MS6 = MC6 × C × SC (7) The gastrocnemius muscle strength score MS7 is calculated using Equation 8 below. MS7 = MC7 × C × SC (8) The muscle strength score MS8 for the abductor and adductor muscles is calculated using Equation 9 below. MS8 = ME8 × E × SE (9) The adductor longus muscle strength score MS9 is calculated using Equation 10 below. MS9 = ME9 × E × SE (10) The sartorius muscle strength score MS10 is calculated using the following Equation 11: MS10 = ME10 × E × SE (11) The above formulas 2 to 11 are a group of calculation formulas for calculating the muscle strength of the evaluation target muscles related to the risk of falling. The above formulas 2 to 11 are an example of a muscle strength estimation model for evaluating the muscle strength of each muscle in response to input scores for five items related to the risk of falling. For example, the muscle strength evaluation unit 134 estimates the muscle strength of the evaluation target muscles using the above formulas 2 to 11. The above formulas 2 to 11 are merely examples and do not limit the muscle strength estimation model for estimating the muscle strength of the evaluation target muscles. Furthermore, the muscles whose muscle strength is estimated are not limited to the above 10. The muscle strength estimation model only needs to be able to estimate the muscle strength of any of the evaluation target muscles in response to input scores for at least one of the five items related to the risk of falling.

[0113] FIG. 19 is a conceptual diagram showing an example of an estimation model 120 that estimates the muscle strength of a muscle to be evaluated using feature amounts extracted from gait waveform data. FIG. 19 shows an example in which there are n muscles to be evaluated (n is a natural number). The estimation model 120 includes a physical ability estimation model 150 and a muscle strength estimation model 156. The feature amounts extracted from the gait waveform data are input to the physical ability estimation model 150, which estimates scores for five items related to the risk of falling. The physical ability estimation model 150 includes estimation models 151, 152, Estimation Model 153 , estimation model 154, and estimation model 155. In response to input of feature amounts extracted from gait waveform data, physical ability estimation model 150 outputs scores S1 to S5 of five items related to fall risk. The scores S1 to S5 of the five items output from physical ability estimation model 150 are input to muscle strength estimation model 156. In response to input of scores S1 to S5 of the five items, muscle strength estimation model 156 outputs muscle strength scores MS1 to MSn related to the muscle strength of the muscles to be evaluated.

[0114] The estimation model 151 outputs a score (total muscle strength score S1) related to the total muscle strength (grip strength) of the whole body in response to input of feature amounts AM1 to AM4 or feature amounts AF1 to AF3 extracted from sensor data measured as the user walks. For example, the estimation model 151 may be a different model for men and a different model for women. There are no limitations on the estimation result of the estimation model 151 as long as an estimation result related to an index of total muscle strength is output in response to input of feature amount data for estimating total muscle strength. For example, the estimation model 151 may be a model that estimates dynamic balance using attribute data such as age and height as explanatory variables in addition to feature amounts AM1 to AM4 or feature amounts AF1 to AF3.

[0115] For example, an estimation model 151 that estimates the total muscle strength score S1 using a multiple regression prediction method is stored in the storage unit 132. For example, the storage unit 132 stores parameters for estimating the total muscle strength score S1 using the following equation 12. SA=m(am1×AM1+am2×AM2+am3×AM3+am4×AM4+am0)+f(af1×AF1+af2×AF2+af3×AF3+af0)...(12) In the above equation (12), AM1, AM2, AM3, and AM4 are feature quantities for each walking phase cluster used to estimate the total muscle strength score S1 for men shown in the correspondence table of FIG. 13. am1, am2, am3, and am4 are coefficients (weights) by which AM1, AM2, AM3, and AM4 are multiplied. am0 is a constant term. AF1, AF2, and AF3 are feature quantities for each walking phase cluster used to estimate the total muscle strength score S1 for women shown in the correspondence table of FIG. 13. af1, af2, and af3 are coefficients (weights) by which AF1, AF2, and AF3 are multiplied. af0 is a constant term. m and f are flags according to gender. If the user is male, m is 1 and f is 0. If the user is female, m is 0 and f is 1. For example, am0, am1, am2, am3, am4, af0, af1, af2, and af3 are stored in the storage unit 132.

[0116] The estimation model 152 outputs a score related to dynamic balance (dynamic balance score S2) in response to input of feature amounts B1 to B5 extracted from sensor data measured as the user walks. There are no limitations on the estimation result of the estimation model 152 as long as an estimation result related to a dynamic balance index is output in response to input of feature amount data for estimating dynamic balance. For example, the estimation model 152 may be a model that estimates dynamic balance using attribute data such as height as explanatory variables in addition to the feature amounts B1 to B5.

[0117] For example, an estimation model for estimating the dynamic balance score S2 using a multiple regression prediction method is stored in the storage unit 132. For example, parameters for estimating the dynamic balance score S2 are stored in the storage unit 132 using the following Equation 13. SB=b1×B1+b2×B2+b3×B3+b4×B4+b5×B5+b0...(13) In the above equation 13, B1, B2, B3, B4, and B5 are feature quantities for each walking phase cluster used to estimate dynamic balance, as shown in the correspondence table of FIG. 14. b1, b2, b3, b4, and b5 are coefficients (weights) by which B1, B2, B3, B4, and B5 are multiplied. b0 is a constant term. For example, b0, b1, b2, b3, b4, and b5 are stored in the storage unit 132.

[0118] Estimation Model 153 outputs a score related to lower limb muscle strength (lower limb muscle strength score S3) in response to input of feature amounts C1 to C4 extracted from sensor data measured as the user walks. When an estimation result related to an index of lower limb muscle strength is output in response to input of feature amount data for estimating lower limb muscle strength, Estimation Model 153 There are no limitations on the estimated results. For example, Estimation Model 153 may be a model that estimates dynamic balance using attribute data such as age as explanatory variables in addition to the feature amounts C1 to C4.

[0119] For example, an estimation model for estimating the lower limb muscle strength score S3 using a multiple regression prediction method is stored in the storage unit 132. For example, parameters for estimating the lower limb muscle strength score S3 are stored in the storage unit 132 using the following Equation 14. SC=c1×C1+c2×C2+c3×C3+c4×C4+c0...(14) In the above formula 14, C1, C2, C3, and C4 are feature quantities for each walking phase cluster used to estimate lower limb muscle strength, as shown in the correspondence table of FIG. 15. c1, c2, c3, and c4 are coefficients (weights) by which C1, C2, C3, and C4 are multiplied. c0 is a constant term. For example, c0, c1, c2, c3, and c4 are stored in the storage unit 132.

[0120] The estimation model 154 outputs a score related to mobility (mobility score S4) in response to input of feature amounts D1 to D6 extracted from sensor data measured as the user walks. There are no limitations on the estimation result of the estimation model 154 as long as an estimation result related to a mobility index is output in response to input of feature amount data for estimating mobility. For example, the estimation model 154 may be a model that estimates mobility using attribute data such as age as an explanatory variable in addition to the feature amounts D1 to D6.

[0121] For example, an estimation model for estimating the mobility score S4 using a multiple regression prediction method is stored in the storage unit 132. For example, the storage unit 132 stores parameters for estimating the mobility score S4 using the following equation 15. SD=d1×D1+d2×D2+d3×D3+d4×D4+d5×D5+d6×D6+d0...(15) In the above equation 15, D1, D2, D3, D4, D5, and D6 are feature quantities for each walking phase cluster used to estimate mobility, as shown in the correspondence table of FIG. 16. d1, d2, d3, d4, d5, and d6 are coefficients (weights) by which D1, D2, D3, D4, D5, and D6 are multiplied. d0 is a constant term. For example, d0, d1, d2, d3, d4, d5, and d6 are stored in the storage unit 132.

[0122] The estimation model 155 outputs a score related to static balance (static balance score S5) in response to input of feature amounts E1 to E7 extracted from sensor data measured as the user walks. There are no limitations on the estimation result of the estimation model 155 as long as an estimation result related to a static balance index is output in response to input of feature amount data for estimating static balance. For example, the estimation model 155 may be a model that estimates static balance using attribute data such as age and height as explanatory variables in addition to the feature amounts E1 to E7.

[0123] For example, an estimation model for estimating static balance using a multiple regression prediction method is stored in the storage unit 132. For example, parameters for estimating static balance are stored in the storage unit 132 using the following equation 16. One-leg standing time = e1 × E1 + e2 × E2 + e3 × E3 + e4 × E4 + e5 × E5 + e6 × E6 + e7 × E7 + e0 (16) In the above equation 16, E1, E2, E3, E4, E5, E6, and E7 are feature quantities for each walking phase cluster used to estimate static balance, as shown in the correspondence table of FIG. 17. e1, e2, e3, e4, e5, e6, and e7 are coefficients (weights) by which E1, E2, E3, E4, E5, E6, and E7 are multiplied. e0 is a constant term. For example, e0, e1, e2, e3, e4, e5, e6, and e7 are stored in the storage unit 132.

[0124] The muscle strength estimation model 156 outputs muscle strength scores MS1 to MSn of muscles related to falls in response to input of scores output from the physical ability estimation model 150. That is, the muscle strength estimation model 156 outputs muscle strength scores MS1 to MSn in response to input of scores of five items related to falls. For example, the muscle strength estimation model 156 is configured by the group of calculation formulas of the above-mentioned Equations 2 to 11. The muscle strength estimation model 156 includes estimation model 151, estimation model 152, Estimation Model 153 , estimation model 154, and estimation model 155. That is, the muscle strength estimation model 156 receives at least one of the scores output from estimation model 151, estimation model 152, Estimation Model 153 It is sufficient to input at least one of the scores output from estimation model 154 and estimation model 155. The more scores input to muscle strength estimation model 156, the more accurately muscle strength can be estimated. Physical ability estimation model 150 There is no limitation on the estimation result of the muscle strength estimation model 156, as long as the estimation result regarding muscle strength is output in response to the input of the score output from Physical ability estimation model 150 In addition to the score output from the model, the model may estimate muscle strength using attribute data as explanatory variables.

[0125] (operation) Next, the operation of muscle strength evaluation system 1 will be described with reference to the drawings. Below, we will explain separately gait measurement device 10 and muscle strength evaluation device 13 included in muscle strength evaluation system 1. With regard to gait measurement device 10, we will explain the operation of feature amount data generation unit 12 included in gait measurement device 10.

[0126] [Gait measurement device] Fig. 20 is a flowchart for explaining the operation of the feature amount data generation unit 12 included in the gait measurement device 10. In the explanation following the flowchart of Fig. 20, the feature amount data generation unit 12 will be described as the subject of the operation.

[0127] In FIG. 20, first, the feature amount data generator 12 acquires time-series data of sensor data relating to foot movements (step S101).

[0128] Next, the feature amount data generating unit 12 extracts gait waveform data for one gait cycle from the time series data of the sensor data (step S102). The feature amount data generating unit 12 detects heel strikes and toe lifts from the time series data of the sensor data. The feature amount data generating unit 12 extracts time series data of the sections between successive heel strikes as gait waveform data for one gait cycle.

[0129] Next, the feature amount data generating unit 12 normalizes the extracted walking waveform data for one step cycle (step S103). The feature amount data generating unit 12 normalizes the walking waveform data for one step cycle to a walking cycle of 0 to 100% (first normalization). Furthermore, the feature amount data generating unit 12 normalizes the ratio of the stance phase to the swing phase of the first normalized walking waveform data for one step cycle to 60:40 (second normalization).

[0130] Next, the feature data generating unit 12 extracts feature values ​​from the normalized walking waveform, which are used to estimate five items related to the risk of falling (step S104). For example, the feature data generating unit 12 extracts feature values ​​to be input to a pre-constructed estimation model (first estimation model).

[0131] Next, the feature data generator 12 uses the extracted feature to generate a feature for each walking phase cluster (step S105).

[0132] Next, the feature amount data generator 12 integrates the feature amounts for each walking phase cluster to generate feature amount data for one walking cycle (step S106).

[0133] Next, the feature amount data generating unit 12 outputs the generated feature amount data to the muscle strength evaluation device 13 (step S107).

[0134] [Muscle strength evaluation device] Fig. 21 is a flowchart for explaining the operation of the muscle strength evaluation device 13. In the explanation following the flowchart of Fig. 21, the muscle strength evaluation device 13 will be described as the subject of the operation.

[0135] In FIG. 21, first, the muscle strength evaluation device 13 acquires feature amount data used to estimate the scores of the five items related to the risk of falling (step S111).

[0136] Next, the muscle strength evaluation device 13 inputs the acquired feature data to the physical ability estimation model 150 (step S112). The feature data input to the physical ability estimation model 150 are input to estimation models 151 to 155 that estimate scores for each of the five items related to the risk of falling. Depending on the input feature data, the physical ability estimation model 150 outputs the score for at least one of the five items related to the risk of falling.

[0137] Next, the muscle strength evaluation device 13 inputs the scores related to the five items output from the physical ability estimation model 150 to the muscle strength estimation model 156 (step S113). In response to the input scores, the muscle strength estimation model 156 outputs a muscle strength score for at least one of the muscles to be evaluated that are related to the risk of falling.

[0138] Next, the muscle strength evaluation device 13 evaluates the muscle strength of the muscle to be evaluated according to the output from the muscle strength estimation model 156 (step S114). For example, the muscle strength estimation model 156 evaluates the muscle strength of the muscle to be evaluated according to the score value output from the muscle strength estimation model 156.

[0139] Next, the muscle strength evaluation device 13 outputs information according to the evaluation result regarding the muscle strength of the muscle to be evaluated (step S115). For example, the evaluation result is output to a terminal device (not shown) carried by the user. For example, the evaluation result is output to a system that executes processing using muscle strength.

[0140] (Application example) Next, application examples according to this embodiment will be described with reference to the drawings. In the following application examples, feature amount data measured by a gait measurement device 10 placed in a shoe is used to evaluate the muscle strength of a target muscle related to the risk of falling. For example, the function of the muscle strength evaluation device 13 is installed in a mobile terminal carried by the user.

[0141] 22 to 24 are conceptual diagrams showing an example in which the evaluation results by the muscle strength evaluation device 13 are displayed on the screen of a mobile terminal 160 carried by a user walking while wearing shoes 100 on which a gait measurement device 10 is placed. 22 to 24 show an example in which information corresponding to the evaluation results of muscle strength using feature amount data corresponding to sensor data measured while the user was walking is displayed on the screen of the mobile terminal 160.

[0142] FIG. 22 shows an example of information displayed on the screen of the mobile device 160 according to the muscle strength evaluation results. In the example of FIG. 22, the evaluation result "The muscle strength of muscle 4 has significantly decreased" is displayed on the screen of the mobile device 160. In the example of FIG. 22, a graph showing the muscle strength scores of the muscles being evaluated is also displayed on the screen of the mobile device 160. The graph in FIG. 22 shows a bar graph representing the score for each muscle being evaluated. The graph in FIG. 22 also shows an upper threshold U and a lower threshold L. The upper threshold U indicates the target value for the muscle strength score. Muscles whose muscle strength scores exceed the upper threshold U have sufficient muscle strength. The lower threshold L indicates the evaluation standard value for fall risk. Muscles whose muscle strength scores are below the lower threshold L may be a factor in the risk of falling. In the example of FIG. 22, the muscle strength score of muscle 4 is below the lower threshold L. A user who checks the muscle strength evaluation results displayed on the display unit of the mobile device 160 can check the muscle strength evaluation results and graph to understand the state of their own muscle strength. In the example of FIG. 22, the score for muscle 4 is low. A user who checks the graph displayed on the display unit of mobile terminal 160 can recognize that the strength of his or her own muscle 4 is low. Information about the muscle strength evaluation results may be provided to a party other than the user. For example, information about the muscle strength evaluation results may be output to a terminal device (not shown) used by a trainer who manages the user's physical condition, or by the user's family, etc. For example, information about the muscle strength evaluation results may be recorded in a database (not shown) constructed for the purpose of health management, etc.

[0143] FIG. 23 is another example of information corresponding to the muscle strength evaluation result displayed on the screen of the mobile terminal 160. In the example of FIG. 23, the evaluation result "The strength of muscle 4 has significantly decreased" is displayed on the screen of the mobile terminal 160. Also in the example of FIG. 23, recommendation information corresponding to the muscle strength evaluation result, "Training Z is recommended. Please watch the video below," is displayed on the display unit of the mobile terminal 160. A video related to training that will lead to an increase in the strength of muscle 4, which has significantly decreased, is displayed on the screen of the mobile terminal 160. A user who has checked the information displayed on the display unit of the mobile terminal 160 can practice training that will lead to an increase in the strength of muscle 4 by referring to the video of training Z and exercising in accordance with the recommendation information.

[0144] FIG. 24 is yet another example of information according to the muscle strength evaluation result displayed on the screen of the mobile terminal 160. In the example of FIG. 24, the evaluation result "The muscle strength of muscle 4 has significantly decreased" is displayed on the screen of the mobile terminal 160. Also in the example of FIG. 24, recommendation information according to the muscle strength evaluation result, "Please train according to the training menu below," is displayed on the display unit of the mobile terminal 160. A training menu that will lead to an increase in the muscle strength of muscle 4, which has significantly decreased, is displayed on the screen of the mobile terminal 160. The user who has checked the information displayed on the display unit of the mobile terminal 160 can practice training that will lead to an increase in the muscle strength of muscle 4 by referring to the training menu and exercising in accordance with the recommendation information.

[0145] As described above, the muscle strength evaluation system of this embodiment includes a gait measurement device and a muscle strength evaluation device. The gait measurement device includes a sensor and a feature data generation unit. The sensor has an acceleration sensor and an angular velocity sensor. The sensor measures spatial acceleration using the acceleration sensor. The sensor measures spatial angular velocity using the angular velocity sensor. The sensor generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity. The sensor outputs the generated sensor data to the feature data generation unit. The feature data generation unit acquires time-series data of the sensor data related to foot movement. The feature data generation unit extracts gait waveform data for one walking cycle from the time-series data of the sensor data. The feature data generation unit normalizes the extracted gait waveform data. The feature data generation unit extracts feature values ​​used to estimate the muscle strength of the muscle to be evaluated from the normalized gait waveform data. The feature data generation unit generates feature data including the extracted feature values. The feature data generation unit outputs the generated feature data to the muscle strength evaluation device.

[0146] The muscle strength evaluation device includes a data acquisition unit, a memory unit, an evaluation unit, and an output unit. The data acquisition unit acquires feature data extracted from sensor data related to the user's foot movement, including feature data used to estimate the muscle strength of the muscle to be evaluated that is related to the risk of falling. The memory unit stores an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature data. The evaluation unit inputs the acquired feature data to the estimation model and evaluates the muscle strength of the user's muscle to be evaluated in response to the muscle strength index output from the estimation model. The output unit outputs information related to the evaluation result of the muscle strength of the user's muscle to be evaluated.

[0147] The muscle strength evaluation system of this embodiment evaluates the muscle strength of the user's target muscles using feature quantities extracted from sensor data related to the user's foot movement. Therefore, this embodiment makes it possible to evaluate the muscle strength of muscles related to the risk of falling according to the user's gait in daily life without using any muscle strength evaluation equipment.

[0148] In one aspect of this embodiment, the memory unit stores a physical ability estimation model and a muscle strength estimation model. The physical ability estimation model outputs a physical ability score in response to input of feature amounts used to estimate a physical ability score related to a fall risk. The muscle strength estimation model outputs a muscle strength score of a muscle to be evaluated in response to input of the physical ability score. The data acquisition unit acquires feature amounts used to estimate the physical ability score, extracted from gait waveform data generated using time-series data of sensor data. The evaluation unit inputs the acquired feature amounts into the physical ability estimation model. The evaluation unit inputs the physical ability score output from the estimation model into the muscle strength estimation model. The evaluation unit evaluates the muscle strength of the user's muscle to be evaluated in response to the muscle strength score output from the muscle strength estimation model. According to this aspect, the muscle strength of the user's muscle to be evaluated can be evaluated using an estimation model including the physical ability estimation model and the muscle strength estimation model.

[0149] In one aspect of this embodiment, the memory unit stores a physical ability estimation model and a muscle strength estimation model. The physical ability estimation model is generated by learning using training data for multiple subjects, where feature amounts used to estimate physical ability scores are explanatory variables and the physical ability scores for the multiple subjects are objective variables. The muscle strength estimation model is generated by learning using training data for multiple subjects, where the physical ability scores are explanatory variables and the muscle strength scores of muscles to be evaluated for the multiple subjects are objective variables. The evaluation unit inputs feature amounts used to estimate physical ability scores obtained for the user into the physical ability estimation model. The evaluation unit inputs the physical ability scores output from the estimation model into the muscle strength estimation model. The evaluation unit evaluates the muscle strength of the user's muscles to be evaluated based on the muscle strength scores output from the muscle strength estimation model. According to this aspect, the muscle strength of the user's muscles to be evaluated can be evaluated using an estimation model trained with training data for multiple subjects.

[0150] In one aspect of this embodiment, the storage unit stores an estimation model trained using explanatory variables including attribute data of multiple subjects. The evaluation unit inputs feature and attribute data related to the user into the estimation model and estimates the user's muscle strength based on the user's muscle strength index output from the estimation model. In this aspect, muscle strength is estimated including attribute data that affects the muscle strength of the muscle to be evaluated. Therefore, according to this aspect, the muscle strength of the user's muscle to be evaluated can be evaluated with higher accuracy based on the user's attributes.

[0151] In one aspect of this embodiment, the memory unit stores a physical ability estimation model and a muscle strength estimation model. The physical ability estimation model is generated by learning using training data in which, for a plurality of subjects, feature values ​​used to estimate physical ability scores of physical abilities related to fall risk are used as explanatory variables and the physical ability scores of the plurality of subjects are used as objective variables. The physical abilities related to fall risk are at least one of five physical ability items: total whole-body muscle strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The muscle strength estimation model is generated by learning using training data in which, for a plurality of subjects, physical ability scores related to at least one of the five physical ability items are used as explanatory variables and the muscle strength scores of muscles to be evaluated for the plurality of subjects are used as objective variables. The data acquisition unit acquires feature values ​​used to estimate the physical ability score, extracted from the gait waveform data. The evaluation unit inputs the feature values ​​acquired for the user into the physical ability estimation model. The evaluation unit inputs the physical ability score output from the physical ability estimation model into the muscle strength estimation model. The evaluation unit evaluates the muscle strength of the user's muscles to be evaluated based on the muscle strength score output from the muscle strength estimation model. According to this aspect, the muscle strength of the user's muscles to be evaluated can be evaluated based on physical ability related to the risk of falling.

[0152] In one aspect of this embodiment, the memory unit stores the specific gravity of the muscle to be evaluated related to five physical ability items and the specific gravity coefficient of the muscle to be evaluated that was determined in advance by testing the five physical ability items. The evaluation unit calculates the muscle strength score of the muscle to be evaluated using the product of the specific gravity of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, the specific gravity coefficient of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, and the physical ability score for the physical ability related to the muscle to be evaluated. The evaluation unit evaluates the muscle strength of the muscle to be evaluated based on the calculated muscle strength score of the muscle to be evaluated. According to this aspect, the muscle strength of the muscle to be evaluated can be estimated using the specific gravity and specific gravity coefficient of the muscle to be evaluated in the physical ability related to the muscle to be evaluated.

[0153] In one aspect of the present embodiment, the muscle strength evaluation device is implemented in a terminal device having a screen viewable by a user. For example, the muscle strength evaluation device displays information about the muscle strength of a muscle to be evaluated, estimated based on sensor data, on the screen of the terminal device. For example, the muscle strength evaluation device displays recommendation information corresponding to the muscle strength of the muscle to be evaluated, estimated based on feature quantities extracted from the sensor data, on the screen of the terminal device. For example, the muscle strength evaluation device displays a video about training to strengthen the muscle to be evaluated on the screen of the terminal device as recommendation information corresponding to the muscle strength estimated based on feature quantities extracted from the sensor data. For example, the muscle strength evaluation device displays a training menu for strengthening the muscle to be evaluated on the screen of the terminal device as recommendation information corresponding to the muscle strength estimated based on feature quantities extracted from the sensor data. According to this aspect, by displaying information about the muscle strength of the muscle to be evaluated, estimated based on feature quantities extracted from the sensor data, on a screen viewable by the user, the user can confirm information corresponding to their own muscle strength.

[0154] (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 muscle forces in response to input of feature amounts by learning using feature amount data extracted from sensor data measured by a gait measurement device.

[0155] (composition) FIG. 25 is a block diagram showing an example of the configuration of learning system 2 according to this embodiment. Learning system 2 includes gait measurement device 20 and learning device 25. Gait measurement device 20 and learning device 25 may be connected by wire or wirelessly. Gait measurement device 20 and learning device 25 may be configured as a single device. Alternatively, gait measurement device 20 may be removed from the configuration of learning system 2, and learning system 2 may be configured with only learning device 25. Although FIG. 25 shows only one gait measurement device 20, one gait measurement device 20 may be provided for each foot (two in total). Alternatively, learning device 25 may be configured to perform learning without being connected to gait measurement device 20, using feature data that has been generated in advance by gait measurement device 20 and stored in a database.

[0156] 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 referred to as sensor data). The gait measurement device 20 generates gait waveform data for a normalized stride cycle from the time-series data of the sensor data. The gait measurement device 20 generates feature data used to estimate the muscle strength of evaluation target muscles related to fall risk. For example, the gait measurement device 20 generates feature data used to estimate scores for five items: total whole-body muscle strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The gait measurement device 20 transmits the generated feature data to the learning device 25. The gait measurement device 20 may be configured to transmit the feature data to a database (not shown) accessed by the learning device 25. The feature data stored in the database is used for learning by the learning device 25.

[0157] The learning device 25 receives feature data extracted from walking waveform data of multiple subjects from the gait measurement device 20. When feature data stored in a database (not shown) is used, the learning device 25 receives the feature data from the database. The learning device 25 performs learning using the received feature data. For example, the learning device 25 learns training data in which feature data for estimating scores of five items related to the risk of falling is used as explanatory variables and the scores of the five items corresponding to the feature data is used as a response variable. For example, the learning device 25 learns training data in which at least one of the scores of the five items related to the risk of falling is used as an explanatory variable and the muscle strength score of a muscle to be evaluated is used as a response variable. There are no particular limitations on the learning algorithm executed by the learning device 25. The learning device 25 generates an estimation model trained using training data related to multiple subjects. The learning device 25 stores the generated estimation model. The estimation model trained by the learning device 25 may be stored in a storage device external to the learning device 25.

[0158] [Learning device] Next, details of the learning device 25 will be described with reference to the drawings. Fig. 26 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.

[0159] Receiving unit 251 receives feature amount data from gait measurement device 20. Receiving unit 251 outputs the received feature amount data to learning unit 253. Receiving unit 251 may receive feature amount data from gait measurement device 20 via a wired connection such as a cable, or may receive feature amount data from gait measurement device 20 via wireless communication. For example, receiving unit 251 is configured to receive feature amount data from gait measurement device 20 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of receiving unit 251 may be compliant with standards other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0160] The learning unit 253 acquires feature data from the receiving unit 251. The learning unit 253 performs learning using the acquired feature data. The learning unit 253 generates a physical ability estimation model that outputs scores for five items related to fall risk in response to input of the feature data through learning using the feature data. For example, the learning unit 253 uses feature data extracted from sensor data measured according to the subject's foot movement as explanatory variables and learns a dataset that uses the scores for the five items related to fall risk as objective variables. Furthermore, the learning unit 253 generates a muscle strength estimation model that outputs a muscle strength score for a muscle to be evaluated in response to input of the scores for the five items through learning using the scores for the five items related to fall risk. For example, the learning unit 253 learns training data that uses at least one of the scores for the five items as an explanatory variable and the muscle strength score for the muscle to be evaluated as an objective variable. For example, the learning unit 253 generates an estimation model based on attribute data. The learning unit 253 stores the estimation models learned for multiple subjects in the storage unit 255.

[0161] For example, the learning unit 253 performs learning using a linear regression algorithm. For example, the learning unit 253 performs learning using a support vector machine (SVM) algorithm. For example, the learning unit 253 performs learning using a Gaussian process regression (GPR) algorithm. For example, the learning unit 253 performs learning using a random forest (RF) algorithm. For example, the learning unit 253 may perform unsupervised learning that classifies the subjects that generated feature amount data according to the feature amount data. There are no particular limitations on the learning algorithm performed by the learning unit 253.

[0162] The learning unit 253 may perform learning using walking waveform data for one step gait cycle as explanatory variables. For example, the learning unit 253 performs supervised learning using walking waveform data of acceleration in three axial directions, angular velocity around three axes, and angle around three axes (posture angle) as explanatory variables, and the correct value of the muscle strength index as the objective variable. For example, if the walking phase is set in 1% increments in a walking cycle from 0 to 100%, the learning unit 253 performs learning using 909 explanatory variables.

[0163] FIG. 27 is a conceptual diagram illustrating learning for generating a physical ability estimation model that estimates a physical ability score related to a fall risk. FIG. 27 is a conceptual diagram illustrating an example in which the learning unit 253 learns a data set of feature amounts, which are explanatory variables, and scores, which are objective variables corresponding to those feature amounts, as training data. To train a model that estimates a male's total muscle strength score S1, a data set of feature amounts AM1 to AM4 and the male's total muscle strength score S1 is used as training data. To train a model that estimates a female's total muscle strength score S1, a data set of feature amounts AF1 to AF3 and the female's total muscle strength score S1 is used as training data. To train a model that estimates a dynamic balance score S2, a data set of feature amounts B1 to B5 and the dynamic balance score S2 is used as training data. To train a model that estimates a lower limb muscle strength score S3, a data set of feature amounts C1 to C4 and the lower limb muscle strength score S3 is used as training data. A data set of feature amounts D1-D6 and the mobility score S4 is used as training data for training a model that estimates the mobility score S4. A data set of feature amounts E1-E7 and the static balance score S5 is used as training data for training a model that estimates the static balance score S5. For example, the learning unit 253 learns data on multiple subjects and generates a physical ability estimation model that outputs physical ability scores for five items related to fall risk in response to input of feature amounts extracted from sensor data. In training to generate the physical ability estimation model, multiple estimation models included in the physical ability estimation model may be generated individually, or multiple estimation models may be generated all at once.

[0164] FIG. 28 is a conceptual diagram illustrating learning for generating a muscle strength estimation model. FIG. 28 is a conceptual diagram showing an example in which the learning unit 253 learns using a data set of five item scores as explanatory variables and a muscle strength score as a response variable as training data (n is a natural number). In the example of FIG. 28, training data is used in which the total muscle strength score S1 of the whole body, the dynamic balance score S2, the lower limb muscle strength score S3, the mobility score S4, and the static balance score S5 are used as explanatory variables, and the muscle strength scores MS1 to MSn are used as response variables. For example, the learning unit 253 learns data on multiple subjects and generates a muscle strength estimation model that outputs a muscle strength score (muscle strength index) in response to input of feature amounts extracted from sensor data.

[0165] The storage unit 255 stores estimation models used to estimate the muscle strength of the muscle to be evaluated, which have been learned for multiple subjects. The estimation models stored in the storage unit 255 are used to estimate muscle strength by the muscle strength evaluation device 13 of the first embodiment.

[0166] As described above, the learning system of this embodiment includes a gait measurement device and a learning device. The gait measurement device acquires time-series sensor data related to foot movement. The gait measurement device extracts gait waveform data for one step cycle from the time-series sensor data and normalizes the extracted gait waveform data. The gait measurement device extracts, from the normalized gait waveform data, feature amounts used to evaluate the user's muscles to be evaluated, from a walking phase cluster composed of at least one temporally consecutive walking phase. The gait measurement device generates feature amount data including the extracted feature amounts. The gait measurement device outputs the generated feature amount data to the learning device.

[0167] The learning device has a receiving unit, a learning unit, and a storage unit. The receiving unit acquires feature data generated by the gait measurement device. The learning unit performs learning using the feature data. The learning unit generates an estimation model that outputs muscle forces of muscles to be evaluated in response to input of feature amounts of gait phase clusters extracted from time-series data of sensor data measured as the user walks. The estimation model generated by the learning unit is stored in the storage unit.

[0168] The learning system of this embodiment generates an estimation model using feature amount data measured by a gait measurement device. Therefore, according to this aspect, it is possible to generate an estimation model that enables evaluation of muscle strength related to fall risk according to gait in daily life without using any equipment for evaluating muscle strength.

[0169] In one aspect of this embodiment, the gait measurement device extracts feature quantities related to at least one of five items, namely, total body muscle strength, dynamic balance, lower limb muscle strength, mobility, and static balance, from the normalized gait waveform data. For example, the learning unit generates an estimation model (physical ability estimation model) by learning using training data in which the feature quantities related to at least one of the five items are used as explanatory variables and the scores of the five items corresponding to the feature quantities used as explanatory variables are used as objective variables. The learning unit generates a muscle strength estimation model that outputs a muscle strength score of a muscle to be evaluated in response to input of a score related to at least one of the five items. According to this aspect, a physical ability estimation model can be generated that can estimate scores related to the five items in response to input of feature quantities related to the five items. Furthermore, according to this aspect, a muscle strength estimation model can be generated that can evaluate the muscle strength of a muscle to be evaluated in response to input of scores related to the five items.

[0170] (Third embodiment) Next, a muscle strength evaluation device according to a third embodiment will be described with reference to the drawings. The muscle strength evaluation device of this embodiment has a simplified configuration of the muscle strength evaluation device included in the muscle strength evaluation system of the first embodiment.

[0171] 29 is a block diagram showing an example of the configuration of a muscle strength evaluation device 33 according to this embodiment. The muscle strength evaluation device 33 includes a data acquisition unit 331, a storage unit 332, an evaluation unit 333, and an output unit 335.

[0172] The data acquisition unit 331 acquires feature data extracted from sensor data related to the user's foot movement, including feature amounts used to estimate the muscle strength of the muscles to be evaluated that are related to the risk of falling. The storage unit 332 stores an estimation model that outputs a muscle strength index for the muscles to be evaluated in response to the input of the feature data. The evaluation unit 333 inputs the acquired feature data into the estimation model and evaluates the muscle strength of the user's muscles to be evaluated in response to the muscle strength index output from the estimation model. The output unit 335 outputs information related to the evaluation result regarding the muscle strength of the user's muscles to be evaluated.

[0173] As described above, in this embodiment, the muscle strength of the user's muscles to be evaluated is evaluated using feature amounts extracted from sensor data related to the user's foot movement. Therefore, this embodiment makes it possible to evaluate the muscle strength of muscles related to the risk of falling according to gait in daily life without using any muscle strength evaluation equipment.

[0174] (Hardware) Here, a hardware configuration for executing control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 30 as an example. Note that the information processing device 90 in Fig. 30 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0175] As shown in Fig. 30, an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 30, interface is abbreviated as I / F (Interface). The processor 91, the main storage device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. The processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are also connected to a network such as the Internet or an intranet via the communication interface 96.

[0176] 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.

[0177] The main memory device 92 has an area in which programs are loaded. Programs stored in the auxiliary memory device 93 or the like are loaded into the main memory device 92 by the processor 91. The main memory device 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). Furthermore, a non-volatile memory such as an MRAM (Magnetoresistive Random Access Memory) may be configured / added to the main memory device 92.

[0178] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or flash memory. Note that it is also possible to configure the main storage device 92 to store various data, thereby omitting the auxiliary storage device 93.

[0179] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0180] Input devices such as a keyboard, mouse, and touch panel may be connected to the information processing device 90 as needed. These input devices are used to input information and settings. When a touch panel is used as the input device, the display screen of the display device may also serve as the interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0181] The information processing device 90 may also be equipped with a display device for displaying information. When a display device is equipped, the information processing device 90 preferably includes a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0182] The information processing device 90 may also be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) for reading data and programs from the recording medium, writing the processing results of the information processing device 90 to the recording medium, etc. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0183] The above is an example of a hardware configuration for enabling control and processing according to each embodiment of the present invention. Note that the hardware configuration in FIG. 30 is an example of a hardware configuration for executing control and processing according to each embodiment, and does not limit the scope of the present invention. Furthermore, a program that causes a computer to execute control and processing according to each embodiment is also within the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also within the scope of the present invention. The recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be realized as a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0184] The components of each embodiment may be combined in any manner, and may be realized by software or by a circuit.

[0185] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0186] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) a data acquisition unit that acquires feature data including feature values ​​used to estimate muscle strength of evaluation target muscles related to the risk of falling, the feature values ​​being extracted from sensor data related to the user's foot movements; a storage unit that stores an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; an evaluation unit that inputs the acquired feature amount data into the estimation model and evaluates the muscle strength of the evaluation target muscle of the user in accordance with the muscle strength index output from the estimation model; an output unit that outputs information related to the evaluation result regarding the muscle strength of the user's muscle to be evaluated. (Appendix 2) The storage unit a physical ability estimation model that outputs a physical ability score related to a fall risk in response to input of a feature value used for estimating the physical ability score; a muscle strength estimation model that outputs a muscle strength score of the muscle to be evaluated in response to input of the physical ability score; The data acquisition unit acquiring a feature quantity used to estimate the physical ability score, the feature quantity being extracted from walking waveform data generated using the time-series data of the sensor data; The evaluation unit inputting the acquired feature amount into the physical ability estimation model; The physical ability score output from the estimation model is input into the muscle strength estimation model; 2. The muscle strength evaluation device according to claim 1, which evaluates the muscle strength of the evaluation target muscle of the user according to the muscle strength score output from the muscle strength estimation model. (Appendix 3) The storage unit the physical ability estimation model is generated by learning using training data in which feature quantities used to estimate the physical ability scores for a plurality of subjects are used as explanatory variables and the physical ability scores for the plurality of subjects are used as objective variables; and storing the muscle strength estimation model generated by learning using teacher data for the plurality of subjects, the teacher data having the physical ability scores as explanatory variables and the muscle strength scores of the muscles to be evaluated for the plurality of subjects as objective variables; The evaluation unit inputting the feature amount acquired about the user and used to estimate the physical ability score into the physical ability estimation model; The physical ability score output from the estimation model is input into the muscle strength estimation model; 3. The muscle strength evaluation device according to claim 2, which evaluates the muscle strength of the evaluation target muscle of the user according to the muscle strength score output from the muscle strength estimation model. (Appendix 4) The storage unit storing the estimation model trained using explanatory variables including attribute data of the plurality of subjects; The evaluation unit A muscle strength evaluation device as described in Appendix 3, which inputs feature amounts and attribute data related to the user into the estimation model, and evaluates the muscle strength of the user's muscle to be evaluated based on the muscle strength index of the user output from the estimation model. (Appendix 5) The storage unit the physical ability estimation model is generated by learning using teacher data in which, for the plurality of subjects, feature quantities used to estimate the physical ability scores for at least one of five physical ability items, namely, total muscle strength of the whole body, dynamic balance, lower limb muscle strength, mobility, and static balance, are used as explanatory variables, and the physical ability scores for at least one of the five physical ability items for the plurality of subjects are used as objective variables; the muscle strength estimation model generated by learning using teacher data in which the physical ability scores for at least one of the five physical ability items are used as explanatory variables for the plurality of subjects, and the muscle strength scores of the muscles to be evaluated for the plurality of subjects are used as objective variables; and The data acquisition unit acquiring a feature amount extracted from the walking waveform data and used to estimate the physical ability score; The evaluation unit inputting the feature amount acquired about the user into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; 5. The muscle strength evaluation device according to claim 3, wherein the muscle strength of the evaluation target muscle of the user is evaluated according to the muscle strength score output from the muscle strength estimation model. (Appendix 6) The storage unit storing the specific gravity of the muscle to be evaluated related to the five physical abilities and the specific gravity coefficient of the muscle to be evaluated that has been determined in advance by testing the five physical abilities; The evaluation unit Calculating the muscle strength score of the muscle to be evaluated using the product of the specific gravity of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, the specific gravity coefficient of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, and the physical ability score related to the physical ability related to the muscle to be evaluated; 6. The muscle strength evaluation device according to claim 5, which evaluates the muscle strength of the muscle to be evaluated according to the calculated muscle strength score of the muscle to be evaluated. (Appendix 7) A muscle strength evaluation device according to any one of Supplementary Notes 1 to 6; a gait measurement device having a sensor that is attached to footwear of a user whose muscle strength is to be evaluated, that measures spatial acceleration and spatial angular velocity, that generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity, and that outputs the generated sensor data; and a feature data generation unit that extracts gait waveform data for one walking cycle from time-series data of the sensor data, normalizes the extracted gait waveform data, extracts feature amounts used to estimate muscle strength of muscles that are to be evaluated from the normalized gait waveform data, generates feature data including the extracted feature amounts, and outputs the generated feature data to the muscle strength evaluation device. (Appendix 8) The muscle strength evaluation device includes: implemented in a terminal device having a screen viewable by the user, 8. The muscle strength evaluation system according to claim 7, wherein information regarding the muscle strength of the muscle to be evaluated, evaluated according to features extracted from the sensor data, is displayed on a screen of the terminal device. (Appendix 9) The muscle strength evaluation device includes: A muscle strength evaluation system according to claim 8, which displays recommendation information according to the muscle strength of the muscle to be evaluated, evaluated according to features extracted from the sensor data, on a screen of the terminal device. (Appendix 10) The muscle strength evaluation device includes: 10. The muscle strength evaluation system according to claim 9, wherein a video relating to training for strengthening the muscle to be evaluated is displayed on the screen of the terminal device as the recommendation information according to the muscle strength of the muscle to be evaluated according to the features extracted from the sensor data. (Appendix 11) The muscle strength evaluation device includes: 11. The muscle strength evaluation system according to claim 9 or 10, wherein a training menu for training a body part related to the muscle strength of the muscle to be evaluated is displayed on a screen of the terminal device as the recommendation information according to the muscle strength of the muscle to be evaluated evaluated according to the feature extracted from the sensor data. (Appendix 12) The computer acquiring feature data including feature values ​​used to estimate muscle strength of target muscles related to the risk of falling, the feature values ​​being extracted from sensor data related to the user's foot movements; inputting the acquired feature amount data into an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; evaluate the muscle strength of the evaluation target muscle of the user according to the muscle strength index output from the estimation model; A muscle strength evaluation method that outputs information regarding an evaluation result regarding the muscle strength of the user's muscle to be evaluated. (Appendix 13) A process of acquiring feature data including feature values ​​used to estimate muscle strength of target muscles related to the risk of falling, extracted from sensor data related to the user's foot movements; inputting the acquired feature amount data into an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; A process of evaluating the muscle strength of the evaluation target muscle of the user according to the muscle strength index output from the estimation model; and outputting information relating to the evaluation result regarding the muscle strength of the user's muscle to be evaluated. [Explanation of symbols]

[0187] 1. Muscle strength evaluation system 2. Learning System 10, 20 Gait measurement device 11 Sensors 12 Feature data generation unit 13 Muscle strength evaluation 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 130, 333 Evaluation Department 131, 331 Data acquisition section 132, 332 storage section 133 Physical ability estimation department 134 Muscle Strength Assessment Section 135, 335 output section 251 Receiving unit 253 Learning Department 255 Storage section

Claims

1. a data acquisition means for acquiring feature data including feature values ​​used to estimate muscle strength of target muscles related to the risk of falling, the feature values ​​being extracted from sensor data relating to foot movements in response to the user's walking; a storage means for storing an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; an evaluation means for inputting the acquired feature amount data into the estimation model and evaluating the muscle strength of the evaluation target muscle of the user in accordance with the muscle strength index output from the estimation model; an output means for outputting information relating to the evaluation result regarding the muscle strength of the muscle to be evaluated of the user; The estimation model is a physical ability estimation model that outputs a physical ability score in response to input of a feature used to estimate at least one of five physical ability items related to the muscle to be evaluated, including total muscle strength of the whole body, dynamic balance, lower limb muscle strength, mobility, and static balance; and a muscle strength estimation model that outputs a muscle strength score of the muscle to be evaluated in response to input of the physical ability score, The data acquisition means acquiring a feature quantity used to estimate the physical ability score, the feature quantity being extracted from walking waveform data generated using the time-series data of the sensor data; The evaluation means inputting the acquired feature amount into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; A muscle strength evaluation device that evaluates the muscle strength of the evaluation target muscle of the user based on the muscle strength score output from the muscle strength estimation model.

2. The storage means the physical ability estimation model is generated by learning using training data in which feature quantities used to estimate the physical ability scores for a plurality of subjects are used as explanatory variables and the physical ability scores for the plurality of subjects are used as objective variables; and storing the muscle strength estimation model generated by learning using teacher data for the plurality of subjects, the teacher data having the physical ability scores as explanatory variables and the muscle strength scores of the muscles to be evaluated for the plurality of subjects as objective variables; The evaluation means inputting the feature amount acquired about the user and used to estimate the physical ability score into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; The muscle strength evaluation device according to claim 1 , wherein the muscle strength of the evaluation target muscle of the user is evaluated according to the muscle strength score output from the muscle strength estimation model.

3. The storage means storing the estimation model trained using explanatory variables including attribute data of the plurality of subjects; The evaluation means The muscle strength evaluation device according to claim 2, wherein the feature amounts and the attribute data related to the user are input into the estimation model, and the muscle strength of the muscle to be evaluated of the user is evaluated based on the muscle strength index of the user output from the estimation model.

4. The storage means the physical ability estimation model is generated by learning using teacher data in which, for the plurality of subjects, feature quantities used to estimate the physical ability scores for at least one of five physical ability items, namely, total muscle strength of the whole body, dynamic balance, lower limb muscle strength, mobility, and static balance, are used as explanatory variables, and the physical ability scores for at least one of the five physical ability items for the plurality of subjects are used as objective variables; the muscle strength estimation model generated by learning using teacher data in which the physical ability scores for at least one of the five physical ability items are used as explanatory variables for the plurality of subjects, and the muscle strength scores of the muscles to be evaluated for the plurality of subjects are used as objective variables; and The data acquisition means acquiring a feature amount extracted from the walking waveform data and used to estimate the physical ability score; The evaluation means inputting the feature amount acquired about the user into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; The muscle strength evaluation device according to claim 2 or 3, wherein the muscle strength of the evaluation target muscle of the user is evaluated according to the muscle strength score output from the muscle strength estimation model.

5. The storage means storing the specific gravity of the muscle to be evaluated related to the five physical abilities and the specific gravity coefficient of the muscle to be evaluated that has been determined in advance by testing the five physical abilities; The evaluation means Calculating the muscle strength score of the muscle to be evaluated using the product of the specific gravity of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, the specific gravity coefficient of the muscle to be evaluated in the physical ability related to the muscle to be evaluated, and the physical ability score related to the physical ability related to the muscle to be evaluated; The muscle strength evaluation device according to claim 4 , wherein the muscle strength of the muscle to be evaluated is evaluated based on the calculated muscle strength score of the muscle to be evaluated.

6. The muscle strength evaluation device according to any one of claims 1 to 5, a gait measurement device having a sensor that is attached to footwear of a user whose muscle strength is to be evaluated, that measures spatial acceleration and spatial angular velocity, that generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity, and that outputs the generated sensor data; and a feature data generation means that extracts gait waveform data for one walking cycle from time-series data of the sensor data, normalizes the extracted gait waveform data, extracts feature amounts used to estimate muscle strength of muscles that are to be evaluated from the normalized gait waveform data, generates feature data including the extracted feature amounts, and outputs the generated feature data to the muscle strength evaluation device.

7. The muscle strength evaluation device includes: implemented in a terminal device having a screen viewable by the user, The muscle strength evaluation system according to claim 6 , wherein information about the muscle strength of the muscle to be evaluated, which is evaluated in accordance with the feature amount extracted from the sensor data, is displayed on a screen of the terminal device.

8. The computer acquiring feature data including feature values ​​used to estimate muscle strength of target muscles related to the risk of falling, the feature values ​​being extracted from sensor data relating to foot movements in response to the user's walking; inputting the acquired feature amount data into an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; evaluate the muscle strength of the evaluation target muscle of the user according to the muscle strength index output from the estimation model; outputting information about the evaluation result regarding the muscle strength of the muscle to be evaluated of the user; The estimation model is a physical ability estimation model that outputs a physical ability score in response to input of a feature used to estimate at least one of five physical ability items related to the muscle to be evaluated, including total muscle strength of the whole body, dynamic balance, lower limb muscle strength, mobility, and static balance; and a muscle strength estimation model that outputs a muscle strength score of the muscle to be evaluated in response to input of the physical ability score, In the acquisition, acquiring a feature quantity used to estimate the physical ability score, the feature quantity being extracted from walking waveform data generated using the time-series data of the sensor data; In the evaluation, inputting the acquired feature amount into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; A muscle strength evaluation method for evaluating the muscle strength of the evaluation target muscle of the user based on the muscle strength score output from the muscle strength estimation model.

9. A computer, A process of acquiring feature data including feature values ​​used to estimate muscle strength of target muscles related to the risk of falling, the feature values ​​being extracted from sensor data related to foot movement in response to the user's walking; inputting the acquired feature amount data into an estimation model that outputs a muscle strength index of the muscle to be evaluated in response to input of the feature amount data; A process of evaluating the muscle strength of the evaluation target muscle of the user according to the muscle strength index output from the estimation model; and outputting information related to the evaluation result regarding the muscle strength of the muscle to be evaluated of the user; The estimation model is a physical ability estimation model that outputs a physical ability score in response to input of a feature used to estimate at least one of five physical ability items related to the muscle to be evaluated, including total muscle strength of the whole body, dynamic balance, lower limb muscle strength, mobility, and static balance; and a muscle strength estimation model that outputs a muscle strength score of the muscle to be evaluated in response to input of the physical ability score, In the acquiring process, causing a computer to execute a process of acquiring a feature amount used for estimating the physical ability score, the feature amount being extracted from gait waveform data generated using the time-series data of the sensor data; In the evaluation process, inputting the acquired feature amount into the physical ability estimation model; inputting the physical ability score output from the physical ability estimation model into the muscle strength estimation model; and evaluating the muscle strength of the evaluation target muscle of the user according to the muscle strength score output from the muscle strength estimation model.

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