Patent Application
20230112078
The present disclosure relates to a floor or ground reaction force index estimation system, a floor or ground reaction force index estimation method, and a floor or ground reaction force index estimation program. More specifically, the present disclosure relates to a ground reaction force index estimation system, a ground reaction force index estimation method, and a ground reaction force index estimation program using a wearable sensor.
The ground reaction force index that is computed from the ground reaction force that the foot receives from the ground when the foot lands on the ground during walking or running is directed to an indicator of risk and performance of disability during walking or running (see, for example, A. A. Zadpoor, et al., Clinical Biomechanics 26, pp. 23 to 28). The ground reaction force indices may include, for example, the loading rate, the kicking force, braking force integrated value (braking impulse), and acceleration force integrated value (acceleration impulse).
The ground reaction force index is computed from the change over time of the vertical and horizontal components of the ground reaction force relative to the ground surface during walking or running for one step, i.e., from a time when the foot lands on the ground to a time when the foot leaves the ground during walking or running. The loading rate is directed to a value computed from the angle of initial standing in the vertical component of the ground reaction force, and corresponds to an index of how rapidly the foot is loaded when the foot lands on the ground. The kicking force is directed to a peak value in the vertical component of the ground reaction force and indicates the force generated by kicking. The braking force integrated value is directed to an integral value of the rearward component of the ground reaction force, and corresponds to a braking component by the foot during walking or running. The acceleration force integrated value is directed to an integral value of the forward component of the ground reaction force, and corresponds to an acceleration component by the foot during walking or running.
The ground reaction force is measured by placing a board or sheet ground reaction force meter on the ground and walking or running on the ground reaction force meter. Since the measurement of the ground reaction force using a ground reaction force meter costs high and the measurement environment is limited to a location where the ground reaction force meter is installed, methods for computing the ground reaction force indices using low cost and compact wearable sensors (see, for example, Japanese Laid-Open Patent Publication Nos. 2018-143412 and 2008-298486) have been developed in recent years.
In JP 2008-298486 A, the ground reaction force is computed on the basis of the output value of the pressure sensor, which value corresponds to the force loaded on the pressure sensor attached to the backside of the footwear that contacts the ground. However, this method limits the location of the sensors to be attached, and furthermore, sensors attached to the soles of the feet may interfere with natural walking and running movements. In contrast, in JP 2018-143412 A, motion sensors are used as wearable sensors.
Although deep learning is a major method for computing ground reaction force from acceleration and angular velocity obtained from motion sensors, this method needs a considerable number of input variables and costs relatively computationally high. Accordingly, it is not easily implemented in a computing device built into a wearable sensor, in which computational resources are limited.
An object of the present disclosure is to provide a ground reaction force index estimation system, a ground reaction force index estimation method, and a ground reaction force index estimation program that compute ground reaction force indices at low computational costs on the basis of data obtained from wearable sensors in light of the above problems.
A summary of the first aspect of the present disclosure is directed to a ground reaction force index estimation system including footwear; and an external device connected to the footwear via a wireless line. The footwear includes: a wearable sensor, which measures at least one of triaxial acceleration and triaxial angular velocity, a first storage, which stores physical feature amount in advance, and a computation unit, which computes values to be used as explanatory variables from measurement results by the wearable sensor to compute ground reaction force and ground reaction force indices by multiple regression analysis from the values to be used as explanatory variables computed from the measurement results by the wearable sensor. The external device includes: a second storage, which stores determination reference information as information for determining walking and running motion of a user from the ground reaction force indices, a controller, which evaluates the walking and running motion for the user on the basis of the measurement results by the wearable sensor received from the footwear and the computed ground reaction force indices, and the determination reference information, and a presentation unit, which presents results of the evaluation of the running motion. In the multiple regression analysis, the ground reaction force as a response variable is computed using a multiple regression equation in which a partial regression coefficient at a time when the results computed from the measurement results are used as explanatory variables is computed in advance.
In the first aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may correspond to at least one of the feature amount of the waveform data of the triaxial angular velocity, and the feature amount of the waveform data of the triaxial angular velocity.
In the first aspect of the present disclosure, the feature amount of the waveform data of the triaxial acceleration may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial acceleration, and the feature amount of the waveform data of the triaxial angular velocity may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial angular velocity.
In the first aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include at least one of the step speed (cadence), walking or running speed (speed), the angle at which the foot lands (strike angle), foot velocity (velocity), the height of the foot at the time of walking or running (foot height), and foot position (distance).
In the first aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include a physical feature amount.
In the first aspect of the present disclosure, the physical feature amount may include at least one of the height, weight, or foot length of the user.
In the first aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include walking and running conditions.
In the first aspect of the present disclosure, the walking/running conditions may include at least one of the slope angle of the ground, the hardness of the material of the ground, the friction coefficient of the material of the ground, the hardness of the material of the footwear, and the coefficient of friction of the material of the footwear.
In the first aspect of the present disclosure, the external device may further include an input unit, and the physical feature amount may be entered through the input unit and transmitted from the external device to the footwear.
In the first aspect of the present disclosure, the external device may further include an input unit, and the walking/running conditions may be entered through the input unit and transmitted from the external device to the footwear.
In the first aspect of the present disclosure, the second storage may store the ground reaction force indices, the measurement results, and the changes of each of the ground reaction force indices and the measurement results over time.
A summary of the second aspect of the present disclosure is directed to a ground reaction force index estimation method including the steps of: detecting at least one of triaxial acceleration and triaxial angular velocity; computing values to be used as explanatory variables from at least one of the detected triaxial acceleration and triaxial angular velocity; computing ground reaction force and ground reaction force indices by multiple regression analysis from the computed values to be used as explanatory variables; computing the ground reaction force and ground reaction force indices by multiple regression analysis from results computed from measurement results for evaluating walking and running motion of a user on the basis of the computed ground reaction force indices and determination reference information as information for determining the walking and running motion of the user from the ground reaction force indices; and presenting results of the evaluation of the running motion. In the step of computing the ground reaction force and ground reaction force indices by the multiple regression analysis, the ground reaction force and ground reaction force indices are computed using a multiple regression equation in which a partial regression coefficient at a time when the results computed from the measurement results are used as explanatory variables is computed in advance.
In the second aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may correspond to at least one of the feature amount of the waveform data of the triaxial angular velocity, and the feature amount of the waveform data of the triaxial angular velocity.
In the second aspect of the present disclosure, the feature amount of the waveform data of the triaxial acceleration may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial acceleration, and the feature amount of the waveform data of the triaxial angular velocity may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial angular velocity.
In the second aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include at least one of the step speed (cadence), walking or running speed (speed), the angle at which the foot lands (strike angle), foot velocity (velocity), the height of the foot at the time of walking or running (foot height), and foot position (distance).
In the second aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include a physical feature amount.
In the second aspect of the present disclosure, the physical feature amount may include at least one of the height, weight, and foot length of the user.
In the second aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include walking/running conditions.
In the second aspect of the present disclosure, the walking/running conditions may include at least one of the slope angle of the ground, the hardness of the material of the ground, the friction coefficient of the material of the ground, the hardness of the material of the footwear, and the coefficient of friction of the material of the footwear.
A summary of the third aspect of the present disclosure is directed to a ground reaction force index estimation program causing a computer to execute the functions of: detecting at least one of triaxial acceleration and triaxial angular velocity; computing values to be used as explanatory variables from at least one of the detected triaxial acceleration and triaxial angular velocity; computing ground reaction force and ground reaction force indices by multiple regression analysis from the computed values to be used as explanatory variables; computing the ground reaction force and ground reaction force indices using multiple regression equation in which results computed from measurement results, a feature amount of waveform data of the measurement results, and a physical feature amount are used as explanatory variables, wherein the results are computed from the measurement results for evaluating walking and running motion for a user on the basis of the computed ground reaction force indices and determination reference information as information for determining the walking and running motion of the user from the ground reaction force indices; and presenting results of the evaluation of the running motion.
In the third aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may correspond to at least one of the feature amount of the waveform data of the triaxial angular velocity, and the feature amount of the waveform data of the triaxial angular velocity.
In the third aspect of the present disclosure, the feature amount of the waveform data of the triaxial acceleration may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial acceleration, and the feature amount of the waveform data of the triaxial angular velocity may include at least one of a global maximum value, a global minimum value, an average value, a local maximum value, a local minimum value, and a number of local maximum/minimum values of the waveform data of the triaxial angular velocity.
In the third aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include at least one of the step speed (cadence), walking or running speed (speed), the angle at which the foot lands (strike angle), foot velocity (velocity), the height of the foot at the time of walking or running (foot height), and foot position (distance).
In the third aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include a physical feature amount.
In the third aspect of the present disclosure, the physical feature amount may include at least one of the height, weight, or foot length of the user.
In the third aspect of the present disclosure, the values used as explanatory variables computed from the measurement results may include walking/running conditions.
In the third aspect of the present disclosure, the walking/running conditions may include at least one of the slope angle of the ground, the hardness of the material of the ground, the friction coefficient of the material of the ground, the hardness of the material of the footwear, and the coefficient of friction of the material of the footwear.
The present disclosure is capable of providing a ground reaction force index estimation system, a ground reaction force index estimation method, and a ground reaction force index estimation program that compute ground reaction force indices at low computational costs on the basis of data obtained from wearable sensors.
Next, an embodiment of the present disclosure will be described with reference to the drawings. In the description of the drawings according to the embodiment, the same or similar reference numerals are assigned to the same or similar components. In addition, it is assumed that the drawings each other may include portions that differ from each other in their relationship.
Also, the embodiments illustrate examples of devices and methods for embodying the technical concept of the present disclosure, and the technical concept of the present disclosure does not specify the configuration of the components to the following, for example. The technical concept of the present disclosure may be modified in various ways within the technical scope defined by the claims.
The ground reaction force index estimation system according to the present embodiment will be described with reference to
The footwear 100 is worn by a user on the foot, which may include running shoes, sneakers, leather shoes, and sandals, for example. In
The sensor 120 is directed to a sensor that detects the movement of the footwear 100 as the user wears the footwear 100, and walks and runs. In this embodiment, the sensor 120 corresponds to a sensor that detects triaxial acceleration and/or triaxial angular velocity. The sensor 120 may have, for example, an acceleration sensor that detects triaxial acceleration and an angular velocity sensor that detects triaxial angular velocity.
As shown in
The power supply 111 supplies power to the module 110 and the sensor 120.
The first controller 112 corresponds to a processor that controls the module 110 and the sensor 120. The first controller 112 controls the module 110 and the sensor 120 by executing the control program stored in the first storage 115.
The first communication unit 113 corresponds to a communication interface that communicates with the external device 200. The first communication unit 113 includes a first reception unit 116 and a first transmission unit 117. The first communication unit 113 communicates with the external device 200 by wireless communication. The first communication unit 113 may communicate with the external device 200 by any telecommunications standard if it is capable of communicating with the external device 200.
The first reception unit 116 receives, from the external device 200, the start and end of measurement by the sensor 120, information related to the physical feature amount and/or walking/running conditions of the user used in computing the ground reaction force index by multiple regression analysis by the computation unit 114, and causes the first storage 115 to store the physical feature amount and/or walking/running conditions of the user. Although the physical feature amount of the user corresponds to, for example, height, weight, foot length, and foot width, it may include any other physical feature amount as well. In the present embodiment, the physical feature amount of the user includes at least one of the height, weight, and foot length. Although the walking/running conditions correspond to, for example, the slope angle of the ground, the hardness and friction coefficient of the ground material, and the hardness and friction coefficient of the shoe material, they may include any other walking/running conditions as well.
The first transmission unit 117 transmits, to the external device 200, the status of the measurement by the sensor 120, information related to the ground reaction force index computed by the multiple regression analysis, and the results computed from the measurement results by the sensor 120. Although the results computed from the measurement results by the sensor 120 may correspond to, for example, the step speed (cadence), walking or running speed (speed), angle at which the foot lands (strike angle), foot velocity (velocity), foot height at a time of walking or running (foot height), and foot position (distance) of the user, they may also include any other indices.
The results computed from the measurement results by the sensor 120 may include the results obtained using the triaxial acceleration, the results obtained using the triaxial angular velocity, results obtained using the triaxial acceleration and the triaxial angular velocity, that are measured by the sensor 120. In the present embodiment, the results computed from the measurement results by the sensor 120 include at least one of the step speed (cadence), walking or running speed (speed), the angle at which the foot lands (strike angle), foot velocity (velocity), the height of the foot at a time of walking or running (foot height), and foot position (distance).
The computation unit 114 computes the values used as explanatory variables from the triaxial acceleration and/or triaxial angular velocity measured by the sensor 120. The values used as explanatory variables include the feature amount of the waveform data of the triaxial acceleration and/or triaxial angular velocity. Here, although the feature amount of the waveform data of the triaxial acceleration and/or triaxial angular velocity computed in the computation unit 114 corresponds to, for example, a global maximum value, global minimum value, average value, local maximum value, local minimum value, and a number of local maximum/minimum values of the triaxial acceleration and/or triaxial angular velocity, it may include any other feature amount.
The values used as explanatory variables may also include at least one of the cadence, speed, strike angle, velocity, foot height, and distance. The values used as explanatory variables may further include a physical feature amount of the user. The values used as explanatory variables may further include walking/driving conditions.
As described below, which values are used as the explanatory variables are determined depending on the ground reaction force index desired to be computed by the computation unit 114 when the ground reaction force and the ground reaction force index are computed using the multiple regression equation stored in the first storage 115 and the values to be used as explanatory variables. Also, when the ground reaction force indices to be computed are different from each other, the multiple regression equations and the partial regression coefficients of the multiple regression equations as well as the explanatory variables used to estimate the ground reaction force indices are different from each other as well. That is, the values used as explanatory variables, the multiple regression equations, and the partial regression coefficients of the multiple regression equations are selected according to the ground reaction force indices desired to be computed. The first storage 115 may store a table, for example, showing the correspondence between the ground reaction force indices to be computed and the corresponding explanatory variables, multiple regression equations, and partial regression coefficients of the multiple regression equations, and then the values to be used as explanatory variables, the multiple regression equations, and the partial regression coefficients of the multiple regression equations may be selected according to the ground reaction force indices desired to be computed with reference to the table.
In the present embodiment, the values computed in the computation unit 114 and used as explanatory variables are at least one of the cadence, speed, strike angle, velocity, foot Height, and distance. In the present embodiment, the feature amount of the waveform data of the triaxial acceleration and/or triaxial angular velocity includes at least one of the global maximum and minimum values of the triaxial acceleration. The feature amount of the waveform data of the triaxial acceleration and triaxial angular velocity may further include, for example, the global maximum and minimum values of the triaxial angular velocity.
Further, the computation unit 114 computes the first ground reaction force and the ground reaction force indices using the multiple regression equations stored in the storage 115 and the values used as explanatory variables. In this case, as described above, the first storage may store, for example, a table showing the correspondence between the ground reaction force indices to be computed and the corresponding explanatory variables, multiple regression equations, and partial regression coefficients of the multiple regression equations, and then the multiple regression equations stored in the first storage 115 and the values used as explanatory variables may be selected according to the ground reaction force indices desired to be computed. In the present embodiment, although the ground reaction force index in the computation unit 114 corresponds to loading speed, kicking force, braking force integrated values (braking impulse), and acceleration force integrated values (acceleration impulse), the ground reaction force index computed in the computation unit 114 may include any other ground reaction force indices if they may be computed from the ground reaction force.
The external device 200 may be in any form, e.g., a personal computer, and mobile terminal, if it is capable of transmitting, to the first reception unit 116, information indicating the start and end of measurement by the sensor 120, and the physical feature amount and walking/running conditions to perform multiple regression analysis in the computation unit 114, and receiving, from the first transmission unit 117, the start and end of measurement by the sensor 120 and the ground reaction force indices. For checking the evaluation of walking and running motions in real time with the user wearing the footwear 100 and performing measurements by the sensor 120, it may be desirable the external device 200 may be a portable terminal such as a mobile terminal.
The external device 200 may be in a form that can be attached to a portion of the body, such as a wristwatch or earphones, for example. If the external device 200 is shaped like a wristwatch, for example, the evaluation of walking and running motions may be checked on the display of the wristwatch-shaped external device 200 without interfering with the walking and running motions. If the external device 200 corresponds to, for example, an earphone, the evaluation of walking or running movements may be checked by voice.
As shown in
The second controller 210 corresponds to a processor that controls portions of the external device 200. Specifically, the second controller 210 controls the portions of the external device 200 by executing a control program stored in the second storage 240. The second controller 210 evaluates the walking and running motions of the user wearing the footwear 100 and walking or running on the basis of the received ground reaction force indices and the determination reference information stored in the second storage 240, and transmits the evaluation results to the presentation unit 230.
The determination reference information corresponds to information for determining the walking and running motions of the user on the basis of the ground reaction force indices. The loading rate is directed to an index of how rapidly the load is applied to the foot when the foot lands on the ground. For example, a threshold value for the loading rate may be set as determination reference information in advance, and if the loading rate is greater than the threshold value, the user may be alerted that the foot is overloaded. The braking force integrated value (braking impulse) corresponds to a braking component of the foot during walking and driving. A threshold value of the braking force integrated value is set in advance, and if the braking force integrated value is greater than the threshold value, the user may be determined to perform inefficient running and notified of that. In the same manner, the acceleration force integrated value (acceleration impulse) corresponds to acceleration component of foot during walking and running. A threshold value of the acceleration force integrated value is set in advance, and if the acceleration force integrated value is greater than the threshold value, it may be determined that a large propulsive force has been obtained, and the user may be notified of that.
The second communication unit 220 corresponds to a communication interface that communicates with the footwear 100. The second communication unit 220 includes a second reception unit 221 and a second transmission unit 222. The second communication unit 220 communicates with the footwear 100 via wireless communication. The second communication unit 220 may communicate by any telecommunications standard if it is capable of communicating with the footwear 100.
The presentation unit 230 presents the received ground reaction force index. The presentation unit 230 may, in addition to or in place of the ground reaction force index, present alert and evaluation results to a user using the footwear 100 on the basis of the evaluation results transmitted from the second controller 210. The presentation unit 230 is capable of presenting the alert and evaluation results to the user in text, pictorial or audio form. In addition, the presentation unit 230 may present information notifying the walking or running conditions of the user such as step speed (cadence), and walking or running speed (speed). Input by the user through the input unit 250 may specify which information is to be presented.
In addition to the control program, determination reference information, physical feature amount of the user, and walking/running conditions, the second storage 240 may store the ground reaction force indices computed by multiple regression analysis, results obtained from the measurement by the sensor 120, i.e., for example, the step speed (cadence) of the user, the walking or running speed (speed), the angle at which the foot lands (strike angle), the velocity of the foot (velocity), the height of the foot at a time when the user is walking or running (foot height), and the position of the foot (distance) with the date and time of the measurement by the sensor 120. The second controller 210 is capable of determining the effectiveness of the training of the user, i.e., walking or running training, for example, on the basis of the changes over time in the ground reaction force index and measurement results.
The input unit 250 receives inputs from the user. The user inputs information associated with the physical feature amount of the user used in computing the ground reaction force index by multiple regression analysis in the computation unit 114, i.e., height, weight, and foot length, as well as walking/running conditions through the input unit 250. The second storage 240 stores the physical feature amount of the user and the walking/running conditions entered through the input unit 250.
Although
In addition, the ground reaction force indices stored in the second storage 240 and the data of the change of the measurement results over time may be stored, for example, in a storage that the personal computer includes in place of the second storage 240 with the footwear 100 connected via wireless communication to the personal computer and mobile terminal as external devices.
Next, a method for obtaining a multiple regression equation used to compute the ground reaction force in the computation unit 114 will be described.
A multiple regression equation is generally directed to an equation in which the response variable is expressed as a function of one or more explanatory variables. It may be expressed as a linear function of one or more explanatory variables, or a polynomial equation of the quadratic or higher degree, for example. In this embodiment, the following equation is used as the multiple regression equation.
Y
i=β0+β1xi1+β2xi2+ . . . +βpxip+Ei Equation 1:
Here, Yi is a response variable, which in this embodiment is the ground reaction force, β0 and βn are partial regression coefficients, xin is an explanatory variable, and E is an error term (where n is an integer from 1 to p). The explanatory variables are directed to, in the present embodiment, the feature amount of the waveform data of the triaxial acceleration and triaxial angular velocity, the results computed from the triaxial acceleration and triaxial angular velocity measured by the sensor 120, the physical feature amount of the user, and the walking/running conditions. In the present embodiment, at least one of the maximum value of acceleration waveform data and the minimum value of acceleration waveform data is used as a feature value of wave data of the triaxial acceleration and/or triaxial angular velocity. In the present embodiment, as a result computed from the triaxial acceleration and triaxial angular velocity, at least one of the step speed (cadence), walking or running speed (speed), the angle at which the foot lands on the ground (strike angle), foot velocity (velocity), the height of the foot at a time of walking or running (foot height), and foot position (distance) is used. In the present embodiment, as the physical feature amount of the subject, at least one of the height, weight, and foot length of the subject is used. As the walking/running conditions, in the present embodiment, at least one of the slope angle of the ground, the hardness and friction coefficients of the ground material, and the hardness and friction coefficients of the shoe material is used.
To obtain the partial regression coefficients of the above equation, the ground reaction force was measured and the triaxial acceleration and triaxial angular velocity were measured by the sensor 12. Specifically, the subject walked and ran on the ground reaction force meter with the footwear 100 worn, and the ground reaction force was measured and the triaxial acceleration and triaxial angular velocity were measured simultaneously by the sensor 12. From the triaxial acceleration and triaxial angular velocity obtained from the measurements by the sensor 120, the values to be used as explanatory variables, i.e., at least one of the cadence, speed, strike angle, velocity, foot height, and distance, and the feature amount of the waveform data of the triaxial acceleration and triaxial angular velocity, i.e., at least one of the global maximum value of the waveform data of the acceleration and the global minimum value of the waveform data of the acceleration, were computed, and then from the ground reaction force value obtained from the measurements by the ground reaction force meter, at least one of the computed cadence, speed, strike angle, velocity, foot height, and distance, at least one of the global maximum value of the waveform data of the acceleration and the global minimum value of the waveform data of the acceleration, and at least one of the height, weight, and foot length of the subject, the multiple regression equation was obtained by computing the partial regression coefficients on the basis of the least squares method.
Table 1 below shows the coefficients R2 of determination and the average value Rave of the multiple correlation coefficients computed from the values shown in
The coefficients R2 of determination and the average value Rave of the multiple correlation coefficients take values between 0 and 1, respectively, with values closer to 1 indicating that the measured and estimated values are closer to each other. The coefficients R2 of determination and the average values Rave of the multiple correlation coefficients shown in table 1 above are all greater than or equal to about 7, and the multiple regression equation used in the present embodiment is determined to be reasonable.
Next, an example of the operation of the ground reaction force index estimation system 10 will be described with reference to
In step S801, the input unit 250 receives input of physical feature amount and walking/running conditions from the user, and the physical feature amount and walking/running conditions are transmitted from the second communication unit 220 to the first reception unit 116.
In step S802, the physical feature amount and walking/running conditions received by the first reception unit 116 are stored in the first storage 115, and a multiple regression equation is determined, which is used in computing the ground reaction force indices on the basis of the multiple regression analysis by the computation unit 114.
In step S803, the input unit 250 receives an input from the user to start measurement by the sensor 120, and an input signal to start measurement is transmitted from the second communication unit 220 to the first reception unit 116.
In step S804, the measurement by the sensor 120 is started.
In step S805, the computation unit 114 computes the values to be used as explanatory variables from the triaxial acceleration and triaxial angular velocity measured by the sensor 120, i.e., at least one of the step speed (cadence), the speed of walking or running (speed), the angle at which the foot lands on the ground (strike angle), the velocity of the side of the foot (velocity), the height of the foot at the time of walking or running (foot height), and the position of the foot (distance), and the feature amount of the waveform data of the triaxial acceleration and triaxial angular velocity, i.e. at least one of the global maximum value of the waveform data of the acceleration and the global minimum value of the waveform data of the acceleration.
In step S806, the computation unit 114 computes the ground reaction force on the basis of at least one of the cadence, speed, strike angle, velocity, foot height, and distance computed in step S805, at least one of the global maximum and minimum values of the waveform data of the triaxial acceleration measured by the sensor 120, at least one of the height, weight, and foot length, and at least one of the slope angle of the ground, the hardness and friction coefficient of the ground material, and the hardness and friction coefficient of the shoe material.
In step S807, the ground reaction force indices (loading rate, kicking force, braking force integrated values, and acceleration force integrated values) are computed.
In step S808, the computed ground reaction force index and the results obtained from the measurements by the sensor 120 are transmitted from the first transmission unit 117 to the second communication unit 220 and stored in the second storage 240.
In step S809, the second controller 210 evaluates the walking and running motion of the user on the basis of the ground reaction force index and the determination reference information stored in the second storage 240, and transmits the evaluation results to the presentation unit 230.
In step S810, the input unit 250 receives an input from the user to terminate the measurement by the sensor 120, and the measurement by the sensor 120 is terminated.
As described above, the present disclosure uses data obtained from inexpensive motion sensors to allow ground reaction force indicators that are conventionally obtained using ground reaction force meters to be estimated. This allows the computation of the ground reaction force indices in any measurement environment, which has been difficult with conventional measurement methods. In addition, in comparison to the conventional estimation methods such as those using machine learning, this method is less computationally expensive, facilitating implement in microcomputers with built-in sensors that have limited computational resources, and achieving high versatility. The low computational costs also facilitate real-time processing. Real-time feedback of information during walking and running is effective in transforming the behavior of the person involved and can be a useful technology for, for example, runners seeking to improve or prevent disability and improve performance, and for patients and instructors associated with rehabilitation.
The above disclosure may include various embodiments that are not described here. Therefore, the technical scope of the present disclosure is defined only by the disclosure specific features recited in the claims which are reasonable from the above description.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2021-090235 | May 2021 | JP | national |
This application is a continuation of International Patent Application No. PCT/JP2022/008153 filed Feb. 28, 2022, which claims the benefit of priority to Japanese Patent Application No. 2021-090235 filed May 28, 2021, the contents of which are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2022/008153 | Feb 2022 | US |
| Child | 18053123 | US |
