Muscle synergy control device, method, and program

Abstract

In one embodiment of the present invention, first, muscle activity measurement data is acquired at a prescribed muscle site of a target user, and muscle synergy data at the muscle site of the target user is analyzed on the basis of the acquired muscle activity measurement data. Next, the optimal transportation distance between the muscle synergy data of the target user and reference muscle synergy data analyzed and stored in advance for each of a plurality of reference candidate users is calculated, a reference user having the optimal transportation distance that satisfies a preset condition is selected from among the plurality of reference candidate users, and a stimulus is presented to the muscle site of the target user on the basis of the muscle synergy data of the target user and the reference muscle synergy data of the selected reference user.

Description

Muscle synergy control device, method, and program

[0001] One aspect of this invention relates to a muscle synergy control device, method, and program used, for example, to improve the way a person uses their muscles.

[0002] The human body has a mechanism for the coordinated control of multiple muscles, and this coordinated structure of multiple muscles is called "muscle synergy." Muscle synergy can be analyzed, for example, using Non-negative Matrix Factorization (NMF). NMF is a method that treats a set of numerical values ​​obtained by measuring electromyograms (EMG), which are bioelectric potential signals generated by muscle activity, in a time series at multiple muscle sites as a matrix, and decomposes it into factors of the matrix.

[0003] In sports and musical instrument performance, this muscle synergy analysis can be used to analyze the differences in muscle usage between experts and beginners. Furthermore, by providing feedback stimuli to beginners based on these differences in muscle synergy, it becomes possible to bring their muscle synergy closer to that of experts. For example, Non-Patent Literature 1 reports a case in which differences in arm muscle usage between experts and beginners were analyzed using muscle synergy analysis, using piano scale playing as an example. Based on the analysis results, it was reported that by presenting electrical muscle stimulation (EMS) to the shoulder, where muscle usage differs significantly, the coordinated movement of the shoulder muscles was activated and muscle synergy was improved.

[0004] Niijima, Arinobu, et al. “Muscle synergies learning with electrical muscle stimulation for playing the piano.” Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 2022.

[0005] Incidentally, even among skilled individuals, muscle synergies can differ depending on the characteristics of their body movements. This tendency is particularly pronounced when dealing with complex motor skills. For example, when considering the operation of a golf putter, it is said that even among skilled individuals, muscle synergies differ, as evidenced by the fact that each person has a different grip on the club. Therefore, when trying to improve the muscle synergies of beginners using feedback stimuli based on muscle synergies, it is unclear which skilled individual's muscle synergies to base the feedback stimuli on and how to design them.

[0006] This invention was made in view of the above circumstances and aims to provide a technology that enables the target user to improve their muscle synergy by using an optimal reference user as a model.

[0007] To solve the above problems, one embodiment of the muscle synergy control device or method according to the present invention first acquires muscle activity measurement data at a predetermined muscle site of a target user, and analyzes muscle synergy data at the same muscle site of the target user based on the acquired muscle activity measurement data. Next, it calculates the optimal transport distance between the target user's muscle synergy data and reference muscle synergy data that has been pre-analyzed and stored for each of a plurality of reference candidate users, and selects a reference user from the plurality of reference candidate users whose optimal transport distance satisfies a pre-set condition. Then, based on the target user's muscle synergy data and the selected reference muscle synergy data of the reference user, it presents stimuli to the target user's muscle site.

[0008] According to one aspect of this invention, the optimal transport distance is calculated between the target user's muscle synergy data and the muscle synergy data of a predetermined group of reference candidate users. A reference candidate user whose calculated optimal transport distance satisfies predetermined conditions, for example, the reference candidate user with the smallest optimal transport distance, is selected as the reference user. Then, a stimulus is presented to the target user by referring to the muscle synergy data of the selected reference user. As a result, it becomes possible to improve the target user's muscle synergy by referring to the muscle synergy of the reference user whose muscle synergy is closest to the target user's, that is, whose body usage is closest to the target user's.

[0009] In other words, according to one aspect of this invention, it is possible to provide a technology that enables the improvement of a target user's muscle synergy by using an optimal reference user as a model for that target user.

[0010] Figure 1 is a block diagram showing an example of the hardware configuration of a muscle synergy control device according to one embodiment of the present invention. Figure 2 is a block diagram showing an example of the software configuration of a muscle synergy control device according to one embodiment of the present invention. Figure 3 is a flowchart showing an example of the processing procedure and processing content of the muscle synergy control process executed by the control unit of the muscle synergy control device shown in Figure 2. Figure 4 is a flowchart showing an example of the processing procedure and processing content of the reference candidate user data acquisition process among the processing procedures shown in Figure 3. Figure 5 is a flowchart showing an example of the processing procedure and processing content of the optimal transport distance calculation process among the processing procedures shown in Figure 3. Figure 6 is a flowchart showing an example of the processing procedure and processing content of the EMS presentation site selection process among the processing procedures shown in Figure 3. Figure 7 shows an example of the attachment position of the electromyography sensor for a reference candidate user. Figure 8 shows an example of the attachment position of the electromyography sensor and EMS electrodes for a target user. Figure 9A shows an example of muscle synergy analysis data for reference candidate user RU1. Figure 9B shows an example of muscle synergy analysis data for reference candidate user RU2. Figure 9C shows an example of muscle synergy analysis data for reference candidate user RU3. Figure 10 shows an example of a transportation cost matrix used to calculate the optimal transportation distance. Figure 11 shows an example of the calculation result of the optimal transportation distance for muscle synergy between the target user and the reference candidate user.

[0011] Embodiments of this invention will be described below with reference to the drawings.

[0012] [One Embodiment] (Example Configuration) The muscle synergy control device MC is composed of, for example, a personal computer installed in a training gym.

[0013] Figures 1 and 2 are block diagrams showing examples of the hardware and software configurations of a muscle synergy control device (MC), respectively.

[0014] The muscle synergy control device MC includes a control unit 1 that uses a hardware processor such as a Central Processing Unit (CPU), and a storage unit having a program storage unit 2 and a data storage unit 3, and an input / output interface (hereinafter referred to as I / F) unit 4 are connected to this control unit 1 via a bus 5.

[0015] Multiple electromyography sensors SC1 to SCn and multiple electrical muscle stimulation electrodes (hereinafter also referred to as EMS (Electrical Muscle Stimulation) electrodes) EP1 to EPn are connected to the input / output interface 4, for example, via signal cables. The input / output interface 4 and the electromyography sensors SC1 to SCn and EMS electrodes EP1 to EPn may be connected by a wireless interface employing a low-power data communication standard such as Bluetooth®.

[0016] The electromyography sensors SC1 to SCn are attached to predetermined muscle sites of the target user US and reference candidate users. The electromyography sensors SC1 to SCn measure time-series electromyographic signals representing muscle activity in the aforementioned muscle sites and output the measured electromyographic signals.

[0017] EMS electrodes EP1 to EPn are attached to multiple muscle areas of the target user US, and according to the EMS signals output from the input / output I / F4 of the muscle synergy control device MC, they present electrical muscle stimulation (EMS) to the aforementioned muscles of the target user US.

[0018] The program storage unit 2 is configured, for example, as a storage medium, by combining a non-volatile memory that can be written to and read at any time, such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), with a non-volatile memory such as ROM (Read Only Memory). In addition to middleware such as an OS (Operating System), it stores various programs necessary to execute various control processes according to one embodiment of this invention.

[0019] The data storage unit 3 is configured, for example, as a storage medium, by combining a non-volatile memory that can be written to and read at any time, such as an HDD or SSD, with a volatile memory such as RAM (Random Access Memory). Its storage area includes an electromyography measurement data storage unit 31, a muscle synergy data storage unit 32, and a reference candidate user data storage unit 33.

[0020] The electromyography measurement data storage unit 31 stores the electromyogram signals of the target user US, which are output from the electromyography sensors SC1 to SCn and received by the input / output I / F unit 4, as electromyography measurement data, associating them with muscle site identification information (hereinafter also referred to as muscle site ID).

[0021] The muscle synergy data storage unit 32 stores muscle synergy data corresponding to each muscle region of the target user US, which has been analyzed from the electromyography measurement data, in association with the muscle region ID. If there are multiple target users US, the muscle synergy data also includes the identification information of the target users (hereinafter referred to as the target user ID).

[0022] The reference candidate user data storage unit 33 stores muscle synergy data previously acquired for multiple muscle regions of multiple reference candidate users, all of whom are experts, in association with muscle region IDs and identification information of reference candidate users (hereinafter also referred to as reference candidate user IDs).

[0023] The control unit 1, as a processing function according to one embodiment of the present invention, includes an electromyography measurement data acquisition processing unit 11, a muscle synergy analysis processing unit 12, an optimal transport distance calculation processing unit 13, a reference user selection processing unit 14, an EMS presentation site selection processing unit 15, and an EMS presentation control processing unit 16.

[0024] Each of these processing units 11 to 16 is implemented by having the hardware processor of the control unit 1 execute the application program stored in the program storage unit 2. Note that some or all of the above processing units 11 to 16 may be implemented using hardware such as LSI (Large Scale Integration) or ASIC (Application Specific Integrated Circuit).

[0025] The electromyography measurement data acquisition processing unit 11 receives electromyogram signals output from electromyography sensors SC1 to SCn via the input / output interface unit 4, converts each received electromyogram signal into digital data at a predetermined sampling period, and stores it in the electromyography measurement data storage unit 31 in association with muscle site IDs.

[0026] The muscle synergy analysis processing unit 12 reads electromyography (EMG) measurement data from multiple muscle sites of the target user US from the EMG measurement data storage unit 31, and analyzes the muscle synergy corresponding to each muscle site from the read EMG measurement data. The muscle synergy data obtained as a result of the analysis is then stored in the muscle synergy data storage unit 32, associated with the muscle site ID. An example of the muscle synergy analysis process will be described in the operation example.

[0027] The optimal transport distance calculation processing unit 13 calculates the optimal transport distance between the muscle synergy data of the target user US stored in the muscle synergy data storage unit 32 and the muscle synergy data of multiple reference candidate users previously stored in the reference candidate user data storage unit 33. An example of the optimal transport distance calculation process will also be explained in the operation example.

[0028] The reference user selection processing unit 14 compares the optimal transport distances calculated for each reference candidate user by the optimal transport distance calculation processing unit 13 and selects the reference candidate user whose optimal transport distance satisfies predetermined conditions, for example, the one with the shortest distance, as the reference user.

[0029] The EMS presentation area selection processing unit 15 calculates the difference between the muscle synergy data of the reference user selected by the reference user selection processing unit 14 and the muscle synergy data of the target user US, for each muscle area. Based on the calculation results, it selects the area to be stimulated by EMS, that is, the muscle area to be activated. An example of this EMS presentation area selection process will also be explained in the operation example.

[0030] The EMS prompt control processing unit 16 generates an EMS signal by controlling predetermined parameters related to the EMS. Then, the generated EMS signal is output from the input / output I / F unit 4 to the EMS electrodes MP1 to MPn corresponding to the stimulation presentation site selected by the above-mentioned EMS presentation site selection processing unit 15.

[0031] (Operation example) Next, an operation example of the muscle synergy control device MC configured as described above will be described.

[0032] In one embodiment, a case where the way the body is used when the target user US performs a putter operation in golf is corrected by muscle synergy control will be described as an example.

[0033] FIG. 3 is a flowchart showing an example of the processing procedure and processing content of the muscle synergy control processing executed by the control unit 1 of the muscle synergy control device MC.

[0034] (1) Acquisition of reference candidate user data The control unit 1 of the muscle synergy control device MC first performs a process of acquiring muscle synergy data of a plurality of reference candidate users who are experts in step S1 and registering it in advance in the reference candidate user data storage unit 33. In this example, a case where data of three reference candidate users RU1 to RU3 is registered will be described as an example.

[0035] FIG. 4 is a flowchart showing an example of the processing procedure and processing content of the reference candidate user data acquisition process.

[0036] (1-1) Acquisition of electromyogram measurement data First, one reference candidate user is selected, and electromyogram sensors SC1 to SCn are attached to a plurality of muscle sites of the selected reference candidate user (for example, RU1). For example, taking the case of performing a putter operation in golf as an example, electromyogram sensors SCX are respectively attached to the skin at sites corresponding to a total of 10 muscles related to the movement of the body during the putter operation, such as the superficial flexor digitorum, extensor digitorum communis, biceps brachii, triceps brachii, and deltoid muscles of both arms. Then, the electromyogram sensors SC1 to SCn are respectively connected to the muscle synergy control device MC by signal cables. As a result, power is supplied from the muscle synergy control device MC to the electromyogram sensors SC1 to SCn, and the electromyogram sensors SC1 to SCn are in an operable state.

[0037] In this state, the reference candidate user RU1 is asked to perform a golf putting operation. Figure 7 shows an example of this, where SC1 to SCn represent electromyography sensors.

[0038] The control unit 1 of the muscle synergy control device MC receives time-series electromyogram signals output from electromyogram sensors SC1 to SCn via the input / output interface unit 4 in step S12, under the control of the electromyogram measurement data acquisition processing unit 11, while the reference candidate user RU1 is operating the putter. The electromyogram measurement data acquisition processing unit 11 then samples each received electromyogram signal, converts the data into digital data, and stores the converted data in the electromyogram measurement data storage unit 31 as electromyogram measurement data, with the converted data associated with the muscle site ID and the reference candidate user ID.

[0039] The putting action can be performed only once, or it can be repeated multiple times over a predetermined period, for example, one minute.

[0040] (1-2) Analysis of muscle synergy When electromyography measurement data is acquired, the control unit 1 of the muscle synergy control device MC performs the following process to analyze muscle synergy from the electromyography measurement data under the control of the muscle synergy analysis processing unit 12.

[0041] Specifically, the muscle synergy analysis processing unit 12 first applies a filter to the electromyography measurement data of each muscle site read from the electromyography measurement data storage unit 31 in step S13 to remove noise, and then calculates the root mean square (RMS) from the electromyography measurement data after the noise removal in step S13.

[0042] Next, in step S15, the muscle synergy analysis processing unit 12 divides the calculated RMS value by the RMS value at maximum voluntary movement for each muscle region, which has been measured in advance and stored in the storage area of ​​the data storage unit 3, thereby calculating the %MVC (Maximum Voluntary Contraction) value.

[0043] Next, in step S16, the muscle synergy analysis processing unit 12 applies non - negative matrix factorization (NMF) to the % MVC for each muscle part to calculate muscle synergies. At this time, the number of muscle synergies to be calculated is a preset number, for example, two. The muscle synergy analysis processing unit 12 stores the two pieces of muscle synergy data calculated for each muscle part in the reference candidate user data storage unit 33 in a state associated with the ID of the reference candidate user TU1 in step S17. Note that the analysis process of muscle synergies using NMF is described in detail in, for example, Non - Patent Document 1, so the description here is omitted.

[0044] (1 - 3) Determination of Completion of Acquisition of Reference Candidate User Data When the analysis process of muscle synergy data for the electromyogram measurement data of one reference candidate user RU1 is completed, the control unit 1 of the muscle synergy control device MC determines in step S18 whether or not the selection of all the reference candidate users RU1 to RU3 has been completed. As a result of this determination, if there is an unselected reference candidate user remaining, the process returns to step S11 to select the next reference candidate user (for example, RU2), and a series of processes from the acquisition of the above - described electromyogram measurement data to the analysis and storage of muscle synergies are executed. Similarly, the above - described series of processes are repeatedly executed for all the remaining reference candidate users. Then, when the series of processes for all the reference candidate users RU1 to RU3 are completed, the control unit 1 ends the acquisition process of muscle synergy data for the plurality of reference candidate users.

[0045] FIGS. 9A to 9C show an example of muscle synergy data obtained for each of the reference candidate users RU1 to RU3. In this example, the flexor digitorum superficialis (FDS) of the right arm R , extensor digitorum communis (FD) R , biceps brachii (BB) R , triceps brachii (TB) R and deltoid (D) R , and for the flexor digitorum superficialis (FDS) of the left arm L , extensor digitorum communis (FD) L , biceps brachii (BB) L , triceps brachii (TB) L and deltoid (D) L shows a case where two pieces of muscle synergy data MS1 and MS2 are obtained for each of the 10 muscles.

[0046] (2) Acquisition of target user data When the target user US starts training, in step S2, the control unit 1 of the muscle synergy control device MC performs the following process, from acquiring electromyography data of the user US who is the target of training to analyzing muscle synergies. This process of acquiring electromyography data of the target user US and analyzing muscle synergies is performed in the same way as the process for the reference candidate user described above.

[0047] Specifically, for the target user US, as with the reference candidate users RU1 to RU3, electromyography sensors SC1 to SCn are attached to the skin corresponding to a total of 10 muscles in both arms that are involved in the body movements when performing a golf putt. These muscles consist of the superficial flexor digitorum, extensor digitorum communis, biceps brachii, triceps brachii, and deltoid muscles. In addition, EMS electrodes MP1 to MPn for EMS presentation are attached to the skin corresponding to the above 10 muscle locations.

[0048] Figure 8 shows an example of a user US with electromyography sensors SC1 to SCn and EMS electrodes MP1 to MPn attached to both arms.

[0049] In this state, the target user US is asked to perform putting operations. During the period when the target user US is performing putting operations, the control unit 1 of the muscle synergy control device MC receives the electromyogram signals output from the electromyogram sensors SC1 to SCn via the input / output interface unit 4, under the control of the electromyogram measurement data acquisition processing unit 11. The received electromyogram signals are then converted into digital data by sampling, and the converted electromyogram measurement data is stored in the electromyogram measurement data storage unit 31, associated with the muscle site ID.

[0050] The control unit 1 of the muscle synergy control device MC then calculates muscle synergies for each muscle site by applying non-negative matrix factorization (NMF) to the electromyography measurement data, under the control of the muscle synergy analysis processing unit 12. In this case as well, the number of muscle synergies to be calculated is, for example, two for each muscle site. The muscle synergy analysis processing unit 12 stores the two muscle synergy data calculated for each muscle site in the muscle synergy data storage unit 32, associating them with the muscle site ID.

[0051] (3) Calculation of optimal transport distance The control unit 1 of the muscle synergy control device MC then solves the optimal transport problem in step S3 under the control of the optimal transport distance calculation processing unit 13, using the spatial components of the target user US's muscle synergy data and the spatial components of the muscle synergy data of the three reference candidate users RU1 to RU3, thereby calculating the optimal transport distance between the target user US's muscle synergy data and the muscle synergy data of the reference candidate users RU1 to RU3.

[0052] Figure 5 is a flowchart showing an example of the processing procedure and content of the optimal transport distance calculation process.

[0053] In other words, the optimal transport distance calculation processing unit 13 first sets the transport cost matrix, which is one of the parameters necessary to calculate the optimal transport distance, in step S31. The transport cost matrix is ​​designed based on the biomechanics of the human body. For example, the transport cost between the muscles of the right arm and the muscles of the left arm is set to a large value. Also, the transport costs between the flexor digitorum superficialis and extensor digitorum communis, which are antagonistic muscles, and between the biceps brachii and triceps brachii are set to be somewhat large values ​​even between muscles of the same arm. Specifically, the transport cost between muscles of the same arm is set to "1", the transport cost between antagonistic muscles of the same arm is set to "2", and the transport cost between muscles of different arms is set to "3".

[0054] Figure 10 shows an example of a set transportation cost matrix. In this example, the FDS of the flexor digitorum superficialis muscle of the right arm... R Extensor digitorum communis FD R , biceps BB R , triceps TB R and deltoid muscle D R And the flexor digitorum superficialis (FDS) of the left arm L Extensor digitorum communis FD L , biceps BB L , triceps TB L and deltoid muscle D L This shows the case where the transportation costs between these 10 muscle locations are set.

[0055] The optimal transport distance calculation processing unit 13 then selects one of the three reference candidate users RU1 to RU3 (for example, RU1) in step S32. Then, in step S33, it applies the set transport cost matrix to calculate the distance that minimizes the transport cost between the probability distribution of the target user US's muscle synergy in the probability space and the probability distribution of the selected reference candidate user RU1's muscle synergy in the probability space. Since there are two muscle synergies for each target user US and reference candidate user RU1, the optimal transport distance calculation processing unit 13 calculates the optimal transport distance for each of the two muscle synergies MS1 and MS2. Then, it calculates the average value of the optimal transport distances calculated for each muscle synergy MS1 and MS2, and uses the calculated average value as the representative value of the optimal transport distance.

[0056] When the optimal transport distance calculation processing unit 13 has finished calculating the optimal transport distance for one reference candidate user, in step S34 it determines whether the selection of all reference candidate users has been completed. If, as a result of this determination, there are still unselected reference candidate users, it returns to step S31 to select the next reference candidate user (for example, RU2), and calculates the optimal transport distance for muscle synergy between the selected reference candidate user RU2 and the target user US. Similarly thereafter, the optimal transport distance for muscle synergy is calculated between reference candidate user RU3 and the target user US. When the calculation of the optimal transport distance for muscle synergy between all reference candidate users RU1 to RU3 and the target user US has been completed, the control unit 1 of the muscle synergy control device MC proceeds to the reference user selection process.

[0057] (4) The control unit 1 of the reference user selection muscle synergy control device MC then, in step S4, under the control of the reference user selection processing unit 14, selects the most suitable reference user to be used as a model for correcting the body movements related to the putting operation of the target user US, as follows:

[0058] In other words, the reference user selection processing unit 14 compares the optimal transport distances calculated in the optimal transport distance calculation processing unit 13 between the muscle synergy of the target user US and the muscle synergies of the three reference candidate users. Based on the results of this comparison, the reference candidate user with the smallest optimal transport distance is selected as the reference user. As a result, the reference user whose body usage related to putting is closest to that of the target user US is selected.

[0059] Figure 11 illustrates an example of the above-described reference user selection process. In this example, the optimal transport distance (OTD) values ​​calculated between the muscle synergy of the target user US and the muscle synergies of each reference candidate user RU1 to RU3 are "0.93", "0.60", and "0.75", respectively. Therefore, the reference user selection processing unit 14 selects reference candidate user RU2 as the reference user, as it has the smallest optimal transport distance (OTD) value of "0.6".

[0060] In the above example, we described the case where the reference user itself is selected, but it is also possible to select the muscle synergy data along with the reference user and provide the selected reference user's muscle synergy data to the next EMS presentation site selection processing unit 15.

[0061] (5) Selection of EMS presentation site The control unit 1 of the muscle synergy control device MC then, in step S5, under the control of the EMS presentation site selection processing unit 15, selects the muscle site to be presented with EMS as follows.

[0062] Figure 6 is a flowchart showing an example of the processing procedure and content of the EMS presentation site selection process.

[0063] In other words, the EMS presentation site selection processing unit 15 first selects one muscle site as a candidate for presentation in step S51. For example, if the muscle sites that are candidates for EMS presentation are the 10 locations corresponding to the flexor digitorum superficialis, extensor digitorum communis, biceps brachii, triceps brachii, and deltoid muscles of the left and right arms, then one of these 10 muscle sites is selected.

[0064] In step S52, the EMS presentation site selection processing unit 15 calculates the difference between the probability space weights of the muscle synergy of the target user US stored in the muscle synergy data storage unit 32 and the probability space weights of the muscle synergy of the reference user RU2 for the selected candidate muscle site, and in step S53, temporarily stores the calculated difference value in the work area of ​​the data storage unit 3.

[0065] The EMS presentation site selection processing unit 15 then determines in step S54 whether the selection of all candidate muscle sites has been completed. If, as a result of this determination, there are still unselected muscle sites, the EMS presentation site selection processing unit 15 returns to step S51 to select the next muscle site and calculates the difference in the muscle synergy probability space weights for the selected muscle site. Thereafter, the EMS presentation site selection processing unit 15 similarly calculates the difference in the muscle synergy probability space weights for each unselected muscle site.

[0066] Then, once the difference in the muscle synergy probability space weights is calculated for all candidate muscle sites, the EMS site selection processing unit 15 selects the muscle sites in step S55 where the reference user RU2 has a larger weight in the muscle synergy probability space than the target user US. Then, in step S56, the EMS site selection processing unit 15 sorts the selected muscle sites in descending order of weight difference value and selects a predetermined number of muscle sites to be presented with EMS, starting with those with the largest difference values. The number of muscle sites to be presented with EMS is set to, for example, the top two, but is not limited to this and can be set arbitrarily.

[0067] For example, in the example shown in Figure 11, the superficial digital flexor muscle (FDS) of the left arm L and biceps BB L The selected option is chosen. The reason for limiting the number of selections to two is that if there are too many muscle areas to which EMS is applied, it may become difficult for the target user (US) to interpret the meaning of the feedback, that is, which muscles they should improve their usage of.

[0068] Here, the muscle regions with high weights in the muscle synergy probability space for the reference user RU2 are the muscle regions that are activated within the muscle synergy. Therefore, by applying EMS to the same muscle regions of the target user US that are activated in the reference user, activation of those muscle regions in the target user US can be expected.

[0069] (6) The control unit 1 of the EMS presentation control muscle synergy control device MC then generates an EMS signal in step S6 under the control of the EMS presentation control processing unit 16 as follows.

[0070] In other words, the EMS presentation control processing unit 16 generates an EMS signal by setting the EMS parameters: pulse width, frequency, presentation time, and current value. The generated EMS signal is then output from the input / output I / F unit 4 at regular intervals to the EMS electrodes attached to the two selected presentation sites.

[0071] For example, the EMS presentation control processing unit 16 generates an EMS signal with a pulse width of 200 μs, a frequency of 200 Hz, a presentation time of 200 ms, and a current value of 5 mA, based on the parameters used in home-use low-frequency therapy devices, and outputs this EMS signal once per second.

[0072] As a result, the muscle areas targeted for EMS stimulation in the two locations mentioned above for the target user US, for example, the superficial digital flexor muscle FDS of the left arm. L and biceps BB L Each of these is presented with a tapping sensation, once every second.

[0073] Therefore, the target user US, while putting, experienced stimulation via EMS, which stimulated the superficial digital flexor muscle (FDS) of the left arm. L and biceps BB L By encouraging attention to each of these muscles, it becomes possible to activate them more effectively, and an improvement in muscle synergy can be expected.

[0074] Furthermore, the intensity of the EMS stimulation can range from a weak stimulus that only causes a vibration to a strong stimulus that causes involuntary muscle contractions and body movement. In addition, the areas and number of EMS applications can be determined arbitrarily.

[0075] (Effects) As described above, in one embodiment, muscle synergy data related to the body movements of multiple reference candidate users during putting is registered in advance. In this state, muscle synergy is analyzed from muscle activity measurement data representing the body movements when the target user US practices putting, and the optimal transport distance of the probability distribution in the probability space is calculated between the muscle synergy of the target user US and the muscle synergies of the multiple reference candidate users, and the reference candidate user with the smallest calculated optimal transport distance is selected as the reference user. Then, the difference in the probability space weights between the muscle synergy of the selected reference user and the muscle synergy of the target user US is calculated for each candidate body part to be presented, and the top two body parts with the largest difference in the weights from among the body parts where the muscle synergy probability space weight of the reference user is larger than that of the target user US are selected as the body parts to be presented by MS, and muscle electrical stimulation is presented to the selected body parts to be presented.

[0076] Therefore, from among multiple candidate users, the user whose body movements are most similar to those of the user performing the putting action is selected. As a result, the target user (US) can practice putting by using the body movements of the reference user whose body movements are similar to their own as a model.

[0077] Furthermore, for each body part, the difference in the probability space weights between the reference user's muscle synergy and the target user's muscle synergy is calculated. Based on the calculated difference, the muscle parts with the largest difference are selected, and EMS stimulation is presented to the selected muscle parts. As a result, the target user can accurately recognize the muscle parts that they should be aware of when performing putting operations.

[0078] [Other Embodiments] (1) In one embodiment, the case in which the period of the EMS stimulation timing presented to the target user is set to a fixed value was described as an example. However, it is not limited to this, and for example, the body movements of the target user US may be detected using a camera or an acceleration sensor, and the EMS stimulation timing may be determined to be synchronized with the timing of a specific movement among the body movements.

[0079] (2) The parameters of the EMS may also be set to be variable according to the muscle area being stimulated. For example, the EMS stimulation intensity may be set to be small for muscle areas that are highly sensitive to EMS stimulation, while the EMS stimulation intensity may be set to be large for muscle areas that are less sensitive to EMS stimulation. Furthermore, the parameters of the EMS may be controlled so that the EMS stimulation intensity is increased for muscle areas with a larger difference in the weights of the probability space of muscle synergy.

[0080] (3) In one embodiment, the control of muscle synergies related to body movements when putting in golf was described as an example, but it may also be used to control muscle synergies related to body movements in other sports, martial arts, health exercises such as yoga, rehabilitation, playing musical instruments, cooking, etc.

[0081] (4) In one embodiment, the Muscle Synergy Control Device MC was described using a personal computer installed in a training gym or the like as an example. However, the Muscle Synergy Control Device MC may be composed of a smartphone, tablet, or wearable device owned by the target user US. Furthermore, each processing function of the Muscle Synergy Control Device MC may be provided on a server computer installed on the Web or in the cloud, or may be distributed across multiple personal computers or server computers.

[0082] (5) In addition, the functional configuration of the muscle synergy control device, the processing procedures and content of the processing performed by each processing function, the types and content of muscle synergy analysis processing, the processing procedures and content for calculating the optimal transport distance between muscle synergies using the optimal transport problem, and the types and number of muscle sites that are the targets for measuring muscle activity and the targets for EMS presentation can also be modified in various ways without departing from the spirit of this invention.

[0083] Although embodiments of this invention have been described in detail above, the above description is merely illustrative in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of this invention. In other words, when implementing this invention, specific configurations may be adopted as appropriate depending on the embodiment.

[0084] In short, this invention is not limited to the embodiments described above, and in the implementation stage, the components can be modified and materialized without departing from the gist of the invention. Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the embodiments. For example, some components may be deleted from all the components shown in the embodiments. Moreover, components from different embodiments may be appropriately combined.

[0085] MC: Muscle synergy control device SC1-SCn: Electromyography sensors MP1-MPn: Electrodes for electrical muscle stimulation (EMS electrodes) US: Target user 1: Control unit 2: Program storage unit 3: Data storage unit 4: Input / output I / F unit 5: Bus 11: Electromyography measurement data acquisition processing unit 12: Muscle synergy analysis processing unit 13: Optimal transport distance calculation processing unit 14: Reference user selection processing unit 15: EMS presentation site selection processing unit 16: EMS presentation control processing unit 31: Electromyography measurement data storage unit 32: Muscle synergy data storage unit 33: Reference candidate user data storage unit

Claims

1. A muscle synergy control device comprising: a first processing unit that acquires muscle activity measurement data in a predetermined muscle area of ​​a target user; a second processing unit that analyzes muscle synergy data in the muscle area of ​​the target user based on the muscle activity measurement data; a storage medium that stores pre-analyzed reference muscle synergy data for the muscle area of ​​a plurality of reference candidate users; a third processing unit that calculates the optimal transport distance between the muscle synergy data of the target user and the reference muscle synergy data of the plurality of reference candidate users; a fourth processing unit that selects a reference user from the plurality of reference candidate users whose optimal transport distance satisfies pre-set conditions; and a fifth processing unit that presents stimuli to the muscle area of ​​the target user based on the muscle synergy data of the target user and the reference muscle synergy data of the selected reference user.

2. The muscle synergy control device according to claim 1, wherein the second processing unit analyzes the muscle synergy data of the target user for each of the multiple muscle sites, the storage medium stores the analyzed reference muscle synergy data for each of the multiple reference candidate users for each of the multiple muscle sites, the fifth processing unit calculates the difference between the muscle synergy data of the target user and the reference muscle synergy data of the selected reference user for each of the muscle sites, selects a specific muscle site from the multiple muscle sites whose reference muscle synergy data is greater than the muscle synergy data of the target user based on the calculated difference, and presents the stimulus to the selected specific muscle site of the target user.

3. A muscle synergy control method executed by an information processing device, comprising: a step of acquiring muscle activity measurement data in a predetermined muscle region of a target user; a step of analyzing muscle synergy data in the muscle region of the target user based on the muscle activity measurement data; a step of storing pre-analyzed reference muscle synergy data for the muscle region of a plurality of reference candidate users in a storage medium; a step of calculating the optimal transport distance between the muscle synergy data of the target user and the reference muscle synergy data of the plurality of reference candidate users; a step of selecting a reference user from among the plurality of reference candidate users whose optimal transport distance satisfies a pre-set condition; and a step of presenting a stimulus to the muscle region of the target user based on the muscle synergy data of the target user and the reference muscle synergy data of the selected reference user.

4. A program that causes a processor in a muscle synergy control device to execute at least one of the processes performed by the first to fifth processing units in the muscle synergy control device according to claim 1 or 2.

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