Estimation device, estimation method, estimation program, and measurement device

Abstract

A novel technique for estimating the state of a human body is provided. [Solution] The estimation device 20 comprises a measurement result acquisition unit 31 that acquires measurement results related to the load when the subject walks, a walking simulator 32 that simulates human walking using a dynamics model that models the mechanical structure of the human leg and human body parameters that represent the physical characteristics of the human leg, and a human body parameter estimation unit 33 that estimates the values ​​of the subject's human body parameters by simulating the subject's walking using the walking simulator 32 while changing the values ​​of the human body parameters and searching for values ​​of the human body parameters that reproduce the measurement results.

Description

[Technical Field]

[0001] The present disclosure relates to a technique for estimating a state of a human body. [Background technology]

[0002] In recent years, people have become more health conscious, and attention has been focused on measuring and managing their daily health. Currently, health devices that estimate body fat percentage and body composition based on static measurements are becoming widespread. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Kenichiro Tokunaga, Bo Liu, Takeshi Inoue, Akira Heya, and Hiroshi Nasuno, "Study on viscoelastic parameter distribution of midsole aiming at minimizing leg joint load by gait analysis using a multibody leg model," Proceedings of the JSME Dynamics and Design Conference 2023, p. 421 Summary of the Invention [Problem to be solved by the invention]

[0004] To maintain good health, it is desirable to be able to easily obtain a variety of health index data. The inventors have focused on the fact that walking behavior is closely related to health status and have come up with a novel method for estimating health index data by analyzing walking behavior.

[0005] The present disclosure has been made in view of such problems, and its purpose is to provide a novel technique for estimating the state of a human body. [Means for solving the problem]

[0006] In order to solve the above problems, an estimation device according to one aspect of the present disclosure includes a measurement result acquisition unit that acquires measurement results relating to the load when a subject walks, a walking simulator that simulates human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent the physical characteristics of a human leg, and a human body parameter estimation unit that estimates the values ​​of the subject's human body parameters by simulating the subject's walking using the walking simulator while changing the values ​​of the human body parameters and searching for values ​​of the human body parameters that reproduce the measurement results.

[0007] Another aspect of the present disclosure is an estimation method, comprising the steps of: acquiring, in a computer, measurement results relating to loads when a subject walks; simulating human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent physical properties of the human leg; and estimating the human body parameters of the subject by simulating the subject's walking while changing the human body parameters and searching for human body parameters that reproduce the measurement results.

[0008] Yet another aspect of the present disclosure is a measurement device including a platform on which a subject places their feet when walking, and a detector for detecting a load acting on the platform, the detector detecting vertical loads acting on a plurality of positions on the platform when the subject walks.

[0009] Any combination of the above components, and conversion of the present disclosure into a method, device, system, recording medium, computer program, etc., are also valid aspects of the present disclosure. [Effects of the Invention]

[0010] According to the present disclosure, a novel technique for estimating the state of a human body can be provided. [Brief explanation of the drawings]

[0011] [Figure 1]FIG. 1 is a diagram illustrating a configuration of a measurement system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of a dynamics model. [Figure 3] FIG. 10 is a diagram illustrating an example of the configuration of the sole of the foot in contact with the earth in a dynamics model. [Figure 4] FIG. 1 is a diagram illustrating an example of the configuration of a measurement device according to the present disclosure. [Figure 5] FIG. 1 is a diagram illustrating a configuration of an estimation device according to an embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram illustrating an example of a screen displayed on a terminal device. [Figure 7] 1 is a flowchart illustrating the steps of an estimation method according to the present disclosure. [Figure 8] FIG. 10 is a diagram showing an estimation result by the estimation device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] 1 shows the configuration of a measurement system according to an embodiment. The measurement system 1 includes a measurement device 10, an estimation device 20, a terminal device 3, a database server 4, and a communication network 2 connecting these devices.

[0013] The measuring device 10 measures the load applied when the subject walks. The estimation device 20 analyzes the measurement results from the measuring device 10 and estimates the values ​​of human body parameters that represent physical characteristics of the subject's leg muscles, etc. The terminal device 3 presents the estimation results from the estimation device 20 to the subject. The database server 4 accumulates the measurement results from the measuring device 10 and the estimation results from the estimation device 20 and provides them to the estimation device 20 and the terminal device 3. The database server 4 may also store correspondence between the estimation results from the estimation device 20 and illnesses or injuries, and provide them to the estimation device 20 and the terminal device 3.

[0014] The estimation device 20 estimates values ​​of human body parameters using a walking simulator that simulates human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent the physical characteristics of a human leg. The estimation device 20 simulates the walking of a subject using the walking simulator while changing the values ​​of the human body parameters, and estimates the values ​​of the human body parameters of the subject by searching for values ​​of the human body parameters that reproduce the measurement results. The estimation device 20 may estimate the values ​​of the human body parameters of the subject by searching for values ​​of the human body parameters that reproduce indices calculated from the load measured by the measurement device 10. The indices may include a ground reaction force (GRF) from the sole of the foot and a center of pressure (COP).

[0015] 2 shows an example of the configuration of a dynamics model. Dynamics model 5 represents the mechanical structure of a person from the knee down. Dynamics model 5 includes a first partial Body1 representing the area from the knee J01 to the ankle J12, a second partial Body2 representing the area from the ankle J12 to the base of the toes J23, a third partial Body3 representing the area from the base of the toes J23 to the tiptoes, nine sole contact points B1 to B9 representing the parts of the sole that come into contact with the ground, a first spring section S12 and a first damper section D12 that imitate the ankle muscles (first muscles) that connect first partial Body1 and second partial Body2, and a second spring section S23 and a second damper section D23 that imitate the toe muscles (second muscles) that connect second partial Body2 and third partial Body3. The first partial Body1 and the second partial Body2 are connected at the ankle J12 so as to be rotatable about a rotation axis perpendicular to the plane of the page. The second partial Body2 and the third partial Body3 are connected at the toe J23 so as to be rotatable about a rotation axis perpendicular to the plane of the page. The sole contact points B1-B6 on the heel side are connected to the second partial Body2, and the sole contact points B7-B9 on the toe side are connected to the third partial Body3. The dimensions, mass, moment of inertia, and other values ​​of each part may be set based on actual measurements of the subject's legs, or may be set to predetermined fixed values ​​depending on the subject's gender, age, height, weight, medical history, etc.

[0016] 3 shows an example of the configuration of the sole contact points in the dynamics model. Each of the sole contact points B1 to B9 is provided with a third spring section S3 and a third damper section D3 that represent the contact force between the sole contact point and the ground, and a fourth spring section S4 and a fourth damper section D4 that represent the friction force between the sole contact point and the ground.

[0017] The human body parameters include a parameter representing the passive characteristics of the ankle muscles, a parameter representing the active characteristics of the ankle muscles, a parameter representing the passive characteristics of the toe muscles, a parameter representing the active characteristics of the toe muscles, a parameter representing the knee movement, and a parameter representing the contact force between the sole of the foot and the ground. The parameters representing the passive characteristics of the ankle muscles include a spring coefficient k12 of the first spring unit S12 and a damping coefficient c12 of the first damper unit D12. The parameters representing the active characteristics of the ankle muscles include the amplitude and phase of the active torque around the ankle J12. The amplitude and phase of the active torque may include higher-order (e.g., second-order, third-order, fourth-order) components. The parameters representing the passive characteristics of the toe muscles include a spring coefficient k23 of the second spring unit S23 and a damping coefficient c23 of the second damper unit D23. Parameters representing the active characteristics of the toe muscles include the amplitude and phase of the active torque around the toe J23. The amplitude and phase of the active torque may include higher-order (e.g., second-order, third-order, fourth-order) components. Parameters representing knee movement include the amplitude and phase of the movement of the knee position in the anterior-posterior (x) direction, the amplitude and phase of the movement of the knee position in the vertical (y) direction, and the amplitude and phase of the movement of the angle of the first part Body1 representing the area from the knee J01 to the ankle J12. The amplitude and phase of the movement of the knee position and angle may include higher-order (e.g., second-order) components. Parameters representing the contact force between the sole and the ground include the spring coefficient k3 of the third spring section S3, the damping coefficient c3 of the third damper section D3, the spring coefficient k4 of the fourth spring section S4, and the damping coefficient c4 of the fourth damper section D4.

[0018] The dynamics model may be modified as appropriate depending on the target, purpose, accuracy, processing capacity of the estimation device 20, and the like. For example, in the above example, the leg from the knee down is represented by three parts, but it may be represented by two parts, or four or more parts. Furthermore, muscles may be represented by translational springs, translational dampers, and active forces. Furthermore, these muscles may be arranged on both sides of Body1 and Body2 to correspond to the triceps surae and tibialis flexor muscles. To more accurately represent muscle arrangement, a new zeroth part, Body0, representing the femur region from the knee J01 up may be added and represented as a translational spring, translational damper, and active force corresponding to the gastrocnemius muscle of the triceps surae. In the above example, nine sole contact earth points are used to represent the sole contact earth points, but the sole contact earth points may be represented by eight or fewer sole contact earth points, or by ten or more sole contact earth points.

[0019] The human body parameters may be appropriately selected or omitted depending on the target, purpose, accuracy, processing capacity of the estimation device 20, etc. For example, the amplitude and phase of the active torque of the sole muscles may be added to the human body parameters. When the processing capacity of the estimation device 20 is low or when estimation needs to be performed in a short time, parameters important for estimating the subject's health state or for ensuring the accuracy of the estimation may be preferentially retained as variables, and other parameters may be omitted or set as constants. The constants may be predetermined based on literature data, existing databases, etc., depending on the subject's gender, age, height, weight, medical history, etc.

[0020] FIG. 4 shows an example configuration of a measurement device according to the present disclosure. The measurement device 10 includes a platform 11 on which the subject's feet are placed when the subject walks, and a detection unit for detecting the load acting on the platform 11. The detection unit includes load sensors 12a, 12b, 12c, and 12d provided at the four corners of the platform 11. The load sensors 12a, 12b, 12c, and 12d detect vertical loads acting at multiple positions on the platform 11 when the subject walks. The distance between the load sensors 12a, 12b, and 12c and 12d in the front-to-back direction may be longer than the length of an average person's foot in the front-to-back direction. The distance between the load sensors 12a, 12c, and 12b and 12d in the left-to-right direction may be longer than the length of an average person's foot in the left-to-right direction. The measuring device 10 shown in the figure is intended to measure the load when a subject stands on the platform 11 from behind with one foot and passes forward, but in another example, it may also measure the load when the subject stands on the platform 11 with both feet and steps. In this case, additional load sensors may be provided in front and behind the left and right feet. That is, load sensors may be provided so as to detect the loads at the four corners where the left foot is placed and the four corners where the right foot is placed. This makes it possible to more accurately measure the loads on the left and right feet when the subject steps in place.

[0021] The detection unit may further include load sensors 12e and 12f that detect the load in the front-to-back direction applied to the platform 11 when the subject walks. The detection unit may further include load sensors 12g and 12h that detect the load in the left-to-right direction applied to the platform 11 when the subject walks. This can further improve the estimation accuracy of the subject's body parameters.

[0022] 5 shows a configuration of an estimation device 20 according to an embodiment of the present disclosure. The estimation device 20 includes a communication device 21, a display device 22, an input device 23, a storage device 24, and a processing device 30. The estimation device 20 may be a server device, a device such as a personal computer, or a mobile terminal such as a mobile phone terminal, a smartphone, or a tablet terminal.

[0023] The communication device 21 controls communication with other devices. The communication device 21 may communicate using any wired or wireless communication method. The display device 22 displays a screen generated by the processing device 30. The display device 22 may be a liquid crystal display device, an organic EL display device, or the like. The input device 23 transmits instructions input by a user or administrator of the estimation device 20 to the processing device 30. The input device 23 may be a mouse, a keyboard, a touchpad, or the like. The display device 22 and the input device 23 may be implemented as a touch panel. The storage device 24 stores programs, data, etc. used by the processing device 30. The storage device 24 may be a semiconductor memory, a hard disk, etc.

[0024] The processing device 30 includes a measurement result acquisition unit 31, a walking simulator 32, a human body parameter estimation unit 33, a health condition estimation unit 34, and an estimation result transmission unit 35. These components are realized in hardware by the CPU, memory, other LSIs, etc. of any computer, and in software by programs loaded into memory, but the functional blocks realized by the cooperation of these components are depicted here. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways, such as by hardware alone or a combination of hardware and software.

[0025] The measurement result acquisition unit 31 acquires measurement results relating to the load when the subject walks from the measurement device 10 and stores them in the storage device 24. The measurement result acquisition unit 31 may acquire the load measured by the measurement device 10 as the measurement result, or may acquire indices such as GRF and COP calculated from the measured load as the measurement result. The measurement result acquisition unit 31 may acquire the load measured by the measurement device 10 and calculate indices such as GRF and COP from the acquired load.

[0026] The walking simulator 32 simulates human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent the physical characteristics of a human leg. The walking simulator 32 may simulate human walking using any known method such as numerical analysis.

[0027] The human body parameter estimation unit 33 estimates the values ​​of the human body parameters of the subject by simulating the walking of the subject using the walking simulator 32 while changing the values ​​of the human body parameters and searching for values ​​of the human body parameters that reproduce the measurement results acquired by the measurement result acquisition unit 31. The human body parameter estimation unit 33 may search for values ​​of the human body parameters that reproduce the measurement results using any known algorithm, such as a gradient method or an experimental design. The human body parameter estimation unit 33 may use, as initial values, values ​​of the human body parameters of the subject that have been estimated in the past and stored in the storage device 24, or values ​​of the human body parameters of other subjects obtained from the database server 4. This enables faster and more accurate estimation of the human body parameters.

[0028] The health state estimation unit 34 estimates the subject's health state based on the estimation results by the human body parameter estimation unit 33. The health state estimation unit 34 may calculate the muscle strength of the muscles in the subject's ankles, toes, and soles, and estimate the possibility of leg disease or injury based on the calculated muscle strength. The health state estimation unit 34 may also estimate the load in the anterior-posterior (x) direction and the vertical (y) direction acting on the knee joints, ankle joints, and toe joints during walking, as well as the load torque for rotational movement. The health state estimation unit 34 may compare the measurement results of the subject's left and right legs to estimate the balance of muscle strength and load. The health state estimation unit 34 may obtain the correspondence between the estimation results and diseases or injuries from the database server 4, and estimate the presence or absence and severity of disease or injury based on the estimation results by the human body parameter estimation unit 33.

[0029] The health state estimation unit 34 may estimate the health state of the subject by comparing the values ​​of the human body parameters of the subject with the values ​​of the human body parameters of other subjects. The health state estimation unit 34 may obtain the values ​​of the human body parameters of other subjects from the database server 4 and compare the values ​​of the human body parameters of the subject estimated by the human body parameter estimation unit 33. The health state estimation unit 34 may compare the values ​​of the human body parameters of the subject with the values ​​of the human body parameters of other subjects who have the same or similar gender, age, height, weight, medical history, etc. as the subject.

[0030] The health state estimation unit 34 may estimate the health state of the subject by comparing the values ​​of the subject's human body parameters with the subject's past values ​​of the human body parameters. The health state estimation unit 34 may obtain the subject's past values ​​of the human body parameters from the storage device 24 or the database server 4 and compare them with the subject's current values ​​of the human body parameters obtained by the human body parameter estimation unit 33. This makes it possible to estimate the transition of the subject's health state.

[0031] The estimation result transmission unit 35 transmits the estimation results by the human body parameter estimation unit 33 and the estimation results by the health state estimation unit 34 to the terminal device 3 and the database server 4. The terminal device 3 presents the estimation results acquired from the estimation device 20 to the subject. The database server 4 stores the estimation results acquired from the estimation device 20 and provides them in response to requests from the terminal device 3 and the estimation device 20.

[0032] FIG. 6 shows an example of a screen displayed on the terminal device 3. The terminal device 3 displays a graph showing the time change in ankle muscle strength estimated by the estimation device 20. The terminal device 3 also displays the results of comparing the subject's current ankle muscle strength with past ankle muscle strength. The terminal device 3 also displays the results of comparing the subject's ankle muscle strength with the average ankle muscle strength of people of the same age.

[0033] FIG. 7 is a flowchart showing the steps of the estimation method of the present disclosure. The measurement result acquisition unit 31 of the estimation device 20 acquires measurement results relating to the load applied when the subject walks from the measurement device 10 (S10). The measurement result acquisition unit 31 calculates the GRF and COP from the acquired load (S12). The human body parameter estimation unit 33 simulates the walking of the subject using the walking simulator 32 while changing the values ​​of the human body parameters, and estimates the values ​​of the human body parameters of the subject by searching for values ​​of the human body parameters that reproduce the measurement results acquired by the measurement result acquisition unit 31 (S14). The health state estimation unit 34 estimates the health state of the subject based on the estimation results by the human body parameter estimation unit 33 (S16). The health state estimation unit 34 estimates the health state of the subject by comparing the values ​​of the human body parameters of the subject with the values ​​of the human body parameters of other subjects (S18). The health state estimation unit 34 estimates the health state of the subject by comparing the values ​​of the human body parameters of the subject with the values ​​of the human body parameters of the subject in the past (S20). The estimation result transmission unit 35 transmits the estimation results by the human body parameter estimation unit 33 and the estimation results by the health state estimation unit 34 to the terminal device 3 and the database server 4 (S22). The terminal device 3 presents the estimation results acquired from the estimation device 20 to the subject (S24).

[0034] FIG. 8 shows the estimation results obtained by the estimation device 20 according to the embodiment. FIG. 8(a1) shows the measured vertical GRF values ​​and the vertical GRF values ​​reproduced using the walking simulator 32. FIG. 8(a2) shows the measured anterior-posterior GRF values ​​and the anterior-posterior GRF values ​​reproduced using the walking simulator 32. FIG. 8(b) shows the measured COP values ​​and the COP values ​​reproduced using the walking simulator 32. It was demonstrated that the measured GRF and COP values ​​can be reproduced with high accuracy by optimizing the values ​​of the human body parameters. Furthermore, FIG. 8(c1) shows the GRF components of the heel (B1-B3), arch (B4-B6), and toe (B7-B9) of the sole of the foot in the vertical GRF values ​​reproduced using the walking simulator 32, and estimates the walking characteristics of the walker. FIG. 8(c2) shows the GRF components of the heel (B1-B3), arch (B4-B6), and toe (B7-B9) of the sole of the foot in the front-to-back direction of the GRF reproduced using the walking simulator 32, and estimates the walking characteristics of the pedestrian. In this way, the pedestrian's body parameters and walking characteristics can be estimated using only the vertical measurement results, only the front-to-back measurement results, or only the left-to-right measurement results. Alternatively, any combination of these measurement results can be used to estimate the pedestrian's body parameters and walking characteristics with even greater accuracy. In particular, using the vertical measurement results can improve the estimation accuracy.

[0035] According to the technology of the present disclosure, simply by walking on the measuring device 10, it is possible to estimate human body parameters such as knee movement, passive and active characteristics of the muscles of the ankle and toes, and passive characteristics of the muscles of the sole of the foot, as well as the load in the anterior-posterior (x) direction and vertical (y) direction acting on the knee joint, ankle joint, and toe joint while walking, as well as the load torque for rotational movement, thereby accurately grasping one's health condition. Furthermore, by comparing one's own past condition and the average for people of the same sex and generation, one can objectively grasp one's own health condition, thereby preventing injuries and illnesses or detecting them early.

[0036] While the present disclosure has been described above based on the embodiments, the embodiments merely illustrate the principles and applications of the present disclosure. Furthermore, many modifications and changes in arrangement are possible to the embodiments without departing from the spirit of the present disclosure as defined in the claims. [Explanation of symbols]

[0037] 1 Measurement system, 2 Communication network, 3 Terminal device, 4 Database server, 5 Dynamics model, 10 Measurement device, 11 Platform unit, 12 Load sensor, 20 Estimation device, 31 Measurement result acquisition unit, 32 Walking simulator, 33 Human body parameter estimation unit, 34 Health condition estimation unit, 35 Estimation result transmission unit.

Claims

1. a measurement result acquisition unit that acquires measurement results relating to the load when the subject walks; a walking simulator that simulates human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent the physical characteristics of a human leg; a human body parameter estimation unit that estimates the values ​​of the human body parameters of the subject by simulating the walking of the subject using the walking simulator while changing the values ​​of the human body parameters and searching for the values ​​of the human body parameters that reproduce the measurement results; An estimation device comprising:

2. The human body parameters include parameters representing passive or active characteristics of the ankle muscles. The estimation device according to claim 1 .

3. The human body parameters include parameters representing passive or active characteristics of the muscles of the toes. The estimation device according to claim 1 .

4. The human body parameters include a parameter representing knee motion. The estimation device according to claim 1 .

5. The human body parameters include a parameter representing a contact force between the sole of the foot and the ground. The estimation device according to claim 1 .

6. the dynamics model includes a first portion representing the area from the knee to the ankle, a second portion representing the area from the ankle to the base of the toes, a third portion representing the area from the base of the toes to the tiptoes, a first spring portion and a first damper portion simulating a first muscle connecting the first portion and the second portion, and a second spring portion and a second damper portion simulating a second muscle connecting the second portion and the third portion; The human body parameters include a spring coefficient of the first spring section, a damping coefficient of the first damper section, an amplitude and a phase of the active torque of the first muscle, a spring coefficient of the second spring section, and a damping coefficient of the second damper section. The estimation device according to claim 1 .

7. The body parameters include the amplitude and phase of the knee's anterior-posterior (x) positional movement, the amplitude and phase of the knee's vertical (y) positional movement, and the amplitude and phase of the lower leg's angular movement. The estimation device according to claim 6 .

8. the dynamics model includes a plurality of contact portions representing contact portions of the soles of the feet with the ground, a third spring portion and a third damper portion representing contact forces with the ground at the contact portions, and a fourth spring portion and a fourth damper portion representing friction forces with the ground at the contact portions, The human body parameters include a spring coefficient of the third spring portion, a damping coefficient of the third damper portion, a spring coefficient of the fourth spring portion, and a damping coefficient of the fourth damper portion. The estimation device according to claim 1 .

9. The measurement results include ground reaction force (GRF) from the sole of the foot and center of pressure (COP). The estimation device according to any one of claims 1 to 8.

10. a health condition estimation unit that estimates the health condition of the subject based on the values ​​of the human body parameters of the subject; The estimation device according to any one of claims 1 to 8.

11. The health condition estimation unit estimates the health condition of the subject by comparing the human body parameter values ​​of the subject with the human body parameter values ​​of other subjects. The estimation device according to claim 10.

12. The health condition estimation unit estimates the health condition of the subject by comparing the values ​​of the human body parameters of the subject with past values ​​of the human body parameters of the subject. The estimation device according to claim 10.

13. On the computer, obtaining measurements of loads exerted by the subject when walking; A step of simulating human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent physical characteristics of the human leg; estimating the human body parameters of the subject by simulating the walking of the subject while changing the human body parameters and searching for the human body parameters that reproduce the measurement results; An estimation method comprising:

14. Computer, a measurement result acquisition unit that acquires measurement results relating to the load when the subject walks; a walking simulator that simulates human walking using a dynamics model that models the mechanical structure of a human leg and human body parameters that represent the physical characteristics of a human leg; a human body parameter estimation unit that estimates the human body parameters of the subject by simulating the walking of the subject using the walking simulator while changing the human body parameters and searching for the human body parameters that reproduce the measurement results; Estimation program to function as.

15. a platform on which the subject's feet rest when the subject walks; a detection unit for detecting a load applied to the base unit; Equipped with The detection unit detects vertical loads applied to a plurality of positions on the platform when the subject walks. Measuring device.

16. The detection unit further detects a load in the front-rear direction applied to the platform when the subject walks.

16. The measuring device of claim 15.

17. The detection unit further detects a load in the left-right direction applied to the platform when the subject walks.

17. The measuring device according to claim 15 or 16.

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