Method and device for coupling human motion capture and muscle-bone dynamics solution

Abstract

The embodiment of the invention provides a method, device and equipment for coupling human body motion capture and muscle-bone dynamics solution and a computer readable storage medium. The method comprises the following steps: acquiring trajectory data of key points when a user performs a specific training activity according to a set target; on the basis of the trajectory data and a human body dynamics principle, calculating a ground reaction force and a moment; based on the trajectory data, the ground reaction force and the torque, calculating the force and the torque borne by each joint through inverse dynamics; based on the acquired muscle activity data, determining a target muscle group through a preset algorithm; comparing the force and moment borne by each joint, the target muscle group and the set target; and correcting the training state of the current user according to the comparison result. In this way, efficient and accurate motion capture is achieved, and meanwhile the user experience is greatly improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of data processing, and more particularly to methods, devices, equipment, and computer-readable storage media for coupling human motion capture and musculoskeletal dynamics solution. Background Art

[0002] With the improvement of living standards and the awakening of health awareness, individuals' attention to their own health has reached an unprecedented level.

[0003] Currently, in-depth analysis of motion data generally relies on professional software such as the OpenSim platform, which can simulate and analyze the human skeletal muscle system based on the collected data and reveal complex biomechanical principles.

[0004] However, traditional analysis methods not only have complex and cumbersome operating procedures, requiring users to go through a series of steps from model establishment, parameter setting to result output, which is not friendly to beginners; but also the analysis results provided are mostly presented in the form of icons or numbers, lacking intuitiveness and readability, and difficult to quickly understand and apply, which greatly limits the actual utilization efficiency and scope of the data. Summary of the Invention

[0005] According to the embodiments of the present application, a solution for coupling human motion capture and musculoskeletal dynamics solution is provided, which can not only achieve efficient and accurate motion capture, but can also be further extended to the comprehensive analysis of human motion forces and torques, greatly improving the user experience.

[0006] In a first aspect of the present application, a method for coupling human motion capture and musculoskeletal dynamics solution is provided. The method comprises: Obtain trajectory data of key points when users perform specific training activities according to set goals; Calculating ground reaction forces and moments based on the trajectory data and human body dynamics principles; Based on the trajectory data, ground reaction force and torque, calculate the force and torque borne by each joint through inverse dynamics; Based on the acquired muscle activity data, the target muscle groups are determined through a preset algorithm; The forces and torques borne by the joints, the target muscle groups, and the set targets are compared; and based on the comparison results, the current user's training status is corrected.

[0007] Furthermore, the obtaining of trajectory data of key points includes: Based on a preset signal quantity mechanism, obtain the original data of N points; where N is a positive integer; Processing the raw data through Kalman filtering to obtain optimized raw data; The optimized original data is expanded by the following formula to obtain the trajectory data of the key points:

[0008] Among them, A is the key point in the optimized original data; is the point to be expanded; is the vector pointing from A to B at the initial moment; is the vector from A at time t to B at the initial time.

[0009] Furthermore, the trajectory data includes angular velocity, speed and / or angle.

[0010] Furthermore, the calculating of the ground reaction force and torque based on the trajectory data and human body dynamics principles includes: Based on the trajectory data and the principles of human body dynamics, the ground reaction force and moment are calculated using the following formula:

[0011] in, is the total external force; N is the total number of object segments; is the mass of each segment; is the linear acceleration of the center of mass; g is gravity; is the total external torque; is the inertia tensor relative to the center of mass of the object segment; 、 are angular velocity and angular acceleration respectively; For each object Forces on endpoints; is the segment of the position vector between the centroid and the endpoints.

[0012] Furthermore, the calculation of the force and torque borne by each joint based on the trajectory data, ground reaction force and torque by inverse dynamics includes: The trajectory data, ground reaction force and torque are subjected to estimated inverse kinematics and inverse dynamics analysis to obtain the force and torque borne by each joint.

[0013] Furthermore, the determining of the target muscle group based on the acquired muscle activity data by a preset algorithm includes: Based on the acquired muscle activity data, the target muscle groups can be determined using the following algorithm:

[0014] Where m is the specific muscle in the muscle group; i is the index vector; q is the generalized coordinate; o is a zero-order matrix; ∈ For muscle force; ∈ is the maximum static force of the muscle; p is the power exponent; is dependent on the generalized coordinate q∈ The moment arm matrix of τ∈ It is a general force.

[0015] Furthermore, it also includes: Through the Unity3D game engine, the status of the user's muscles and bones during training can be visualized.

[0016] In a second aspect of the present application, a device for coupling human motion capture and musculoskeletal dynamics solution is provided. The device comprises: The acquisition module is used to obtain the trajectory data of key points when the user performs specific training activities according to the set goals; a first calculation module, configured to calculate ground reaction forces and moments based on the trajectory data and principles of human body dynamics; A second calculation module is used to calculate the force and torque borne by each joint based on the trajectory data, ground reaction force and torque through inverse dynamics; A determination module, configured to determine target muscle groups based on the acquired muscle activity data using a preset algorithm; The training module is used to compare the forces and torques borne by each joint, the target muscle group, and the set target; and to correct the current user's training status based on the comparison results.

[0017] In a third aspect of the present application, an electronic device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the program.

[0018] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present application is implemented.

[0019] The method for coupling human motion capture and musculoskeletal dynamics solution provided in the embodiment of the present application obtains trajectory data of key points when the user performs specific training activities according to set goals; calculates ground reaction forces and torques based on the trajectory data and human body dynamics principles; calculates the forces and torques borne by each joint through inverse dynamics based on the trajectory data, ground reaction forces and torques; determines the target muscle group based on the acquired muscle activity data through a preset algorithm; compares the forces and torques borne by each joint, the target muscle group, and the set goals; and corrects the current user's training status based on the comparison results, thereby achieving efficient and accurate motion capture and greatly improving the user experience.

[0020] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 is a flow chart of a method for coupling human motion capture and musculoskeletal dynamics solution according to an embodiment of the present application; Figure 2 Schematic diagram of sensor positions according to an embodiment of the present application; Figure 3 41-point motion model according to an embodiment of the present application; Figure 4 Schematic diagram of sensor correction according to an embodiment of the present application; Figure 5 A schematic diagram of conditions required for the current gait state according to an embodiment of the present application; Figure 6 is a block diagram of an apparatus for coupling human motion capture and musculoskeletal dynamics solution according to an embodiment of the present application; Figure 7 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0022] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0023] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0024] Figure 1 A flow chart of a method for coupling human motion capture and musculoskeletal dynamics solution according to an embodiment of the present disclosure is shown. The method includes: S110, obtaining trajectory data of key points when the user performs a specific training activity according to a set goal.

[0025] In some embodiments, different positions and quantities of inertial sensors (IMUs) with Bluetooth communication capabilities, splitters with COM ports, and tights can be deployed according to specific training goals.

[0026] In this disclosure, 15 inertial sensors (IMU) are used as an example. Figure 2 As shown in the figure, inertial sensors (IMUs) are attached to 15 locations on the flexible bodysuit. These IMUs measure the angular velocity, speed, and angle of motion at these 15 locations. After collecting the data, the Kalman filter algorithm is used to analyze it and determine the trajectory of each marker.

[0027] In some embodiments, after acquiring 15 IMU data, the raw data can be processed by the Kalman filter algorithm, and the state prediction and update mechanism can be combined with the observation data to iteratively optimize the IMU position information and velocity estimation, thereby obtaining a smoother and more reliable trajectory of the 15 IMUs, that is, the optimized raw data.

[0028] Through a specific algorithm, the information of the key points in the optimized original data is used to infer and supplement the points around it. For example, Figure 3As shown in the figure, the original 15-point trajectory is expanded to a 41-point trajectory. This process enables the construction of a more refined and comprehensive human body model, improving the expressiveness of model details and the accuracy of motion capture. This expansion method enriches the model's geometric structure by adding additional points while retaining the original key point data. This not only more accurately reflects the complex movements and postures of the human body, but also significantly improves the accuracy of motion capture and the realism of model reconstruction.

[0029] Specifically, let A be a known key point and B be a point near A that needs to be expanded. The vector from A to B at the initial moment is , and the vector from A at time t to B at the initial time is .

[0030] According to the model assumptions:

[0031] Among them, A is the key point in the optimized original data; is the point to be expanded; is the vector pointing from A to B at the initial moment; is the vector from A at time t to B at the initial time; Through the equality relationship between these two vectors, the position of point B added around point A can be calculated.

[0032] In some embodiments, when the user is training, he only needs to wear the flexible device and ensure that each imu is connected to its own Bluetooth serial port, and then perform the initial position correction of the inertial sensor (IMU) (see the correction diagram). Figure 4 ), you can start walking. During this process, the inertial sensor (IMU) will collect raw data from 15 points at a frequency of 50 Hz, generating a raw data list. A semaphore mechanism can be used in data collection to ensure frequency consistency of the 15 serial port IMU data collection.

[0033] In some embodiments, the semaphore mechanism employed in the present disclosure includes: A unified semaphore is defined, and all IMU acquisition tasks start data acquisition after obtaining permission from the semaphore. After each acquisition task is completed, the status is reported to the management module. The management module releases the semaphore after confirming that all tasks are complete, marking the beginning of the next round of data acquisition.

[0034] To ensure consistent acquisition frequency across the 15 IMU sensors, a semaphore mechanism can be used for control. First, a counting semaphore, SS, is created with an initial value of 0, indicating that each IMU must wait for the semaphore to release before starting data acquisition. A separate acquisition thread is created for each IMU sensor (a total of 15 threads), each bound to a specific IMU sensor. When the threads start, all IMU threads call the wait(S) operation to enter a blocked state, waiting for the semaphore to release. The main control thread then calls the signal(S) operation at regular intervals, releasing the semaphore 15 times in succession. This causes all IMU threads to simultaneously unblock and begin data acquisition.

[0035] The IMU thread immediately collects data after the semaphore is released and stores it in a shared buffer. Subsequently, all IMU threads call the wait(S) operation again after completing data collection, entering a blocked state and waiting for the next semaphore release from the main control thread. The main control thread precisely releases the semaphore at a fixed interval (20ms), ensuring that all IMU threads begin collecting data at the same time. This ensures that all IMUs have a completely consistent collection frequency, thus avoiding collection frequency deviations or data asynchrony caused by differences in thread scheduling or operating environments.

[0036] S120: Calculate ground reaction force and torque based on the trajectory data and human body dynamics principles.

[0037] In some embodiments, the present disclosure proposes a GRF&M prediction method based on kinematic information, which can calculate the total external force and external torque using the following formula and apply it to the left and right feet:

[0038] in, is the total external force; N is the total number of object segments; is the mass of each segment; is the linear acceleration of the center of mass; g is gravity; is the total external torque; is the inertia tensor relative to the center of mass of the object segment; 、 are angular velocity and angular acceleration respectively; For each object Forces on endpoints; is the segment of the position vector between the centroid and the endpoint; In the single-support stage, GRF&M is equal to the total external force and external moment; while in the double-support stage, a smooth transition assumption is used to solve the uncertainty problem.

[0039] In some embodiments, gait states and events can be determined by monitoring changes in state of selected values, including contact force magnitude (e.g., force generated when approaching a virtual floor), kinematically estimated lower limb acceleration, and / or equivalent measurements from external sensors (e.g., pressure insoles or accelerometers).

[0040] Further, if Figure 5 As shown, when the selected value changes state, the information is used to , determining whether the gait state is stance or swing. By continuously monitoring the changes in the last k states, gait events and cycles can be identified online. Specifically, the leading leg is assigned to the lower limb corresponding to the last heel strike event until the next heel strike event occurs on the contralateral leg.

[0041] After calculating the parameters related to gait events, the ground reaction force (GRF) and moment (M) of each lower limb are calculated separately. This process ensures the accuracy and reliability of gait analysis, thus providing reliable data support for subsequent evaluation and application.

[0042] S130 , calculating the force and torque borne by each joint through inverse dynamics based on the trajectory data, ground reaction force and torque.

[0043] In some embodiments, estimated inverse kinematics (IK) and inverse dynamics (ID) analysis is performed based on point displacements and angles converted from IMU (inertial measurement unit) data and externally measured ground reaction force and torque data.

[0044] Among them, inverse kinematics is used to calculate the joint angle required to achieve a specific end effector position; inverse dynamics is used to further analyze the forces and torques generated when the joint angle changes, providing information about muscle strength and joint load.

[0045] Furthermore, inverse dynamics analysis can estimate the internal forces and moments acting on joints. This information is crucial for assessing joint biomechanical function, health, and movement efficiency. For example, the load on the knee joint can be accurately calculated by combining factors such as ground reaction force (GRF), joint angle and its rate of change, and limb segment mass distribution.

[0046] S140, based on the acquired muscle activity data, determine the target muscle group through a preset algorithm.

[0047] In some embodiments, a specific optimization algorithm can be applied to determine the most likely muscle group to be activated to minimize errors, i.e., to estimate muscle force (resolving muscle redundancy).

[0048] Specifically, due to muscle redundancy, it is impossible to directly measure muscle strength and all muscle activity, so optimization is one of the only feasible methods. Muscle activity in experimental recordings is interpreted by minimizing objective indicators such as squared muscle stress or squared activation. In the publication, the optimization problem is formulated as a constrained nonlinear optimization problem, and the moment arm matrix is ​​calculated as follows:

[0049] in, ∈ is the force of the muscle; that is, the M-dimensional vector represents the force of all muscles; i is an index vector used to iterate over all muscles; for example represents the force of the i-th muscle; q is a generalized coordinate, q∈R N represents an N-dimensional real vector, where N is the number of generalized coordinates; o is a zero-order matrix; ∈ is the maximum static force of the muscle; p is the power exponent; is dependent on the generalized coordinate q∈ The moment arm matrix of τ∈ It is a general force.

[0050] Furthermore, to speed up the optimization, one can control the convergence tolerance and start from a previously obtained solution. Finally, an interior point algorithm is used to solve nonlinear, constrained optimization problems.

[0051] S150, comparing the forces and torques borne by the joints, the target muscle groups, and the set targets; and correcting the current user's training status based on the comparison results.

[0052] In some embodiments, the forces and torques exerted on each joint, the target muscle groups, and the set goals are compared, and the user's current training status is corrected based on the comparison results. Specifically, based on the comparison results, personalized rehabilitation training recommendations or adjustments to the training plan can be provided to the user to ensure optimal results with each exercise.

[0053] Furthermore, it also includes: To enhance the user experience, the Unity3D game engine can be used to visualize the state of the user's muscles and bones during training. This visualization system records the user's movements and performance, providing immediate feedback. For example, a combo mechanism can be used to encourage users to consistently perform the movements correctly. At the end of a session, both regular points and gold points (which can be redeemed for prizes and other incentives) are calculated and awarded.

[0054] In some embodiments, According to the embodiments of the present disclosure, the following technical effects are achieved: 1. Efficient motion data capture: The motion capture system designed based on the inertial measurement unit (IMU) can collect precise motion data, ensuring the speed and accuracy of data capture.

[0055] 2. Improve user experience: It simplifies the installation and use process of motion capture equipment, improves user wearing comfort and operation convenience, makes it suitable for long-term use, and enhances user-friendliness.

[0056] 3. Enhanced data analysis capabilities: Beyond motion capture, the algorithm can also perform in-depth musculoskeletal dynamics analysis, providing insights into muscle activation patterns, joint load distribution, and force changes, enhancing the value of research and applications.

[0057] 4. Broad application prospects: With its advantages of high precision and applicability, this platform is suitable for a variety of scenarios, from laboratory research to real-life rehabilitation training and personal health monitoring.

[0058] In summary, the method provided by the present disclosure not only improves the efficiency and accuracy of motion data capture and analysis, but also greatly improves the user experience and is applicable to a variety of application scenarios.

[0059] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0060] The above is an introduction to the method embodiment. The following is a device embodiment to further illustrate the solution described in this application.

[0061] Figure 6 A block diagram 600 of an apparatus for coupling human motion capture and musculoskeletal dynamics solution according to an embodiment of the present application is shown. Figure 6 Shown include: An acquisition module 610 is used to acquire trajectory data of key points when a user performs a specific training activity according to a set goal; A first calculation module 620 is configured to calculate ground reaction forces and moments based on the trajectory data and human body dynamics principles; A second calculation module 630 is configured to calculate the forces and moments borne by each joint through inverse dynamics based on the trajectory data, ground reaction forces and moments; A determination module 640 is configured to determine a target muscle group based on the acquired muscle activity data using a preset algorithm; The training module 650 is used to compare the forces and torques borne by each joint, the target muscle group, and the set target; and to correct the current user's training status based on the comparison results.

[0062] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0063] Figure 7 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application is shown.

[0064] like Figure 7 As shown, the terminal device or server includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 into the random access memory (RAM) 703. Various programs and data required for the operation of the terminal device or server are also stored in the RAM 703. The CPU 701, ROM 702 and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0065] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 708 including a hard disk; and a communication section 709 including a network interface card such as a LAN card or a modem. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 710 as needed, so that computer programs read therefrom can be installed into the storage section 708 as needed.

[0066] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present application are executed.

[0067] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0068] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0069] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0070] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the method described in the present application.

[0071] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A method for coupling human motion capture and musculoskeletal dynamics solution, characterized in that: include: Obtain trajectory data of key points when users perform specific training activities according to set goals; Calculating ground reaction forces and moments based on the trajectory data and human body dynamics principles; Based on the trajectory data, ground reaction force and torque, calculate the force and torque borne by each joint through inverse dynamics; Based on the acquired muscle activity data, the target muscle groups are determined through a preset algorithm; Comparing the forces and moments borne by each joint, the target muscle group, and the set target; According to the comparison results, the current user's training status is corrected.

2. The method according to claim 1, characterized in that The acquisition of trajectory data of key points includes: Based on a preset signal quantity mechanism, obtain the original data of N points; where N is a positive integer; Processing the raw data through Kalman filtering to obtain optimized raw data; The optimized original data is expanded by the following formula to obtain the trajectory data of the key points: Among them, A is the key point in the optimized original data; is the point to be expanded; is the vector pointing from A to B at the initial moment; is the vector from A at time t to B at the initial time.

3. The method according to claim 2, characterized in that The trajectory data includes angular velocity, speed and / or angle.

4. The method according to claim 3, wherein: The calculating of the ground reaction force and torque based on the trajectory data and the principle of human body dynamics includes: Based on the trajectory data and the principles of human body dynamics, the ground reaction force and moment are calculated using the following formula: in, is the total external force; N is the total number of object segments; is the mass of each segment; is the linear acceleration of the center of mass; g is gravity; is the total external torque; is the inertia tensor relative to the center of mass of the object segment; 、 are angular velocity and angular acceleration respectively; For each object Forces on endpoints; is the segment of the position vector between the centroid and the endpoints.

5. The method according to claim 4, characterized in that The calculation of the force and torque borne by each joint based on the trajectory data, ground reaction force and torque by inverse dynamics includes: The trajectory data, ground reaction force and torque are subjected to estimated inverse kinematics and inverse dynamics analysis to obtain the force and torque borne by each joint.

6. The method according to claim 5, characterized in that The target muscle group is determined based on the acquired muscle activity data using a preset algorithm, including: Based on the acquired muscle activity data, the target muscle groups can be determined using the following algorithm: Where m is the specific muscle in the muscle group; i is the index vector; q is the generalized coordinate; o is a zero-order matrix; ∈ For muscle force; ∈ is the maximum static force of the muscle; p is the power exponent; is dependent on the generalized coordinate q∈ The moment arm matrix of τ∈ It is a general force.

7. The method according to claim 1, characterized in that Also includes: Through the Unity3D game engine, the status of the user's muscles and bones during training can be visualized.

8. A device for coupling human motion capture and musculoskeletal dynamics solution, characterized in that: include: The acquisition module is used to obtain the trajectory data of key points when the user performs specific training activities according to the set goals; a first calculation module, configured to calculate ground reaction forces and moments based on the trajectory data and principles of human body dynamics; A second calculation module is used to calculate the force and torque borne by each joint based on the trajectory data, ground reaction force and torque through inverse dynamics; A determination module, configured to determine target muscle groups based on the acquired muscle activity data using a preset algorithm; A training module, for comparing the forces and moments borne by each joint, the target muscle group, and the set goals; According to the comparison results, the current user's training status is corrected.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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