Motion detection method and device, electronic equipment and storage medium

Abstract

The invention provides a motion detection method and device, electronic equipment and a storage medium, and relates to the technical field of motion training. The method comprises the following steps: acquiring electromyographic signals of at least two detection positions of a detected object; a motion detection model is called, the electromyographic signals of the at least two detection positions are processed, joint angle information and muscle force information of the at least two detection positions are obtained, and the joint angle information of each detection position is used for indicating the angle of the joint closest to the detection position; and at the target client, according to the joint angle information and the muscle force information of the at least two detection positions, motion prompt information is generated and displayed. According to the method, the user can be prompted when the user performs autonomous training, and the problem that the training effect is not remarkable due to the fact that the user cannot know whether the action is correct or not is avoided.

Description

Technical Field

[0001] The present disclosure relates to the technical field of sports training, and in particular to a motion detection method, device, electronic device, and storage medium. Background Art

[0002] With the development of sports, more and more people are choosing to conduct independent training. Specifically, users can follow instructional videos in the form of live broadcasts and conduct sports training without the supervision of coaches or sparring partners.

[0003] However, in the absence of supervision, most people are unable to confirm whether their movements are in place during independent training, and cannot know whether the movements are correct. As a result, the training effect is not significant, and in severe cases, it may even cause certain harm to the body. Summary of the Invention

[0004] In view of this, the purpose of the present disclosure is to provide a motion detection method, device, electronic device and storage medium, which can specifically solve the existing problems.

[0005] Based on the above-mentioned purpose, in the first aspect, the present disclosure proposes a motion detection method, including: obtaining electromyographic signals of at least two detection positions of the detected object; calling a motion detection model, processing the electromyographic signals of at least two detection positions, and obtaining joint angle information and muscle strength information of at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position; on the target client, generating and displaying motion prompt information based on the joint angle information and muscle strength information of at least two detection positions.

[0006] In the second aspect, a motion detection device is also provided, including: an acquisition unit, configured to acquire electromyographic signals of at least two detection positions of the detected object; a calling unit, configured to call a motion detection model, process the electromyographic signals of at least two detection positions, and obtain joint angle information and muscle strength information of at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position; a generation unit, configured to generate and display motion prompt information on the target client based on the joint angle information and muscle strength information of at least two detection positions.

[0007] In a third aspect, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect.

[0008] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement any method of the first aspect.

[0009] In general, the present disclosure has at least the following beneficial effects: this embodiment can prompt the user through the user's joint angle information and muscle strength information when the user is performing autonomous training, so that the user can confirm whether his or her movements are in place, thereby avoiding the problem of insignificant training effects caused by the user not knowing whether the movements are correct. In addition, the present disclosure can process electromyographic signals through motion detection models, thereby reducing the number of sensors for data collection. In addition, the raw data obtained by the present disclosure can only include electromyographic signals, so that the present disclosure can monitor movement postures only through electromyographic signals, reducing the types and number of sensors, and facilitating the wear of sensor equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments according to the present disclosure and should not be regarded as limiting the scope of the present disclosure.

[0011] Figure 1 A flow chart of a motion detection method according to an embodiment of the present disclosure is shown;

[0012] Figure 2 Another flow chart of a motion detection method according to an embodiment of the present disclosure is shown;

[0013] Figure 3 A schematic diagram of an application scenario according to an embodiment of the present disclosure is shown;

[0014] Figure 4 A schematic diagram of a motion detection device according to an embodiment of the present disclosure is shown;

[0015] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown;

[0016] Figure 6 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0017] The present disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0018] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] Figure 1 The motion detection method of the present disclosure is shown. In an embodiment of the present disclosure, the method includes:

[0020] Step S101: Acquire electromyographic signals of at least two detection positions of the detected object.

[0021] In this embodiment, the execution entity of the motion detection method can acquire or receive myoelectric signals from at least two detection positions of the detected object. The detected object can be any object that generates myoelectric signals, such as a human or an animal. The execution entity can be, for example, a terminal.

[0022] The detection location can be any location on the subject's body surface, such as on the arm, leg, etc.

[0023] The electromyographic signal is a surface electromyographic signal (sEMG), which is the manifestation of the electrophysiological signal of muscle tissue contraction on the skin. It can be measured through electrodes and can well show the force characteristics of muscles during exercise.

[0024] Step S102, calling the motion detection model, processing the electromyographic signals of at least two detection positions, and obtaining joint angle information and muscle force information of at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position.

[0025] In this embodiment, the execution entity may call the motion detection model to process the electromyographic signals of at least two detection positions to obtain joint angle information and muscle force information of the at least two detection positions. Specifically, the execution entity may input the electromyographic signals of at least two detection positions into the motion detection model to obtain the joint angle information and muscle force information of the at least two detection positions outputted by the motion detection model.

[0026] The motion detection model can be various deep learning models, which can not only predict the joint angle of the joint closest to the detection position, but also predict the muscle strength information of the muscle where the detection position is located.

[0027] Step S103: On the target client, motion prompt information is generated and displayed based on the joint angle information and muscle strength information of at least two detection positions.

[0028] In this embodiment, the execution entity may generate and display exercise prompt information on the target client loaded by the terminal. The execution entity may generate the exercise prompt information based on the joint angle information and muscle strength information of at least two detection positions in various ways. For example, the execution entity may directly use the joint angle information and muscle strength information of at least two detection positions as the exercise prompt information.

[0029] This embodiment can prompt the user through the user's joint angle information and muscle strength information when the user is performing autonomous training, so that the user can confirm whether his or her movements are in place, avoiding the problem of insignificant training effects caused by the user not knowing whether the movements are correct. In addition, the present disclosure can process electromyographic signals through a motion detection model, thereby reducing the number of sensors for data collection. In addition, the raw data obtained by the present disclosure can only include electromyographic signals, so that the present disclosure can monitor movement postures only through electromyographic signals, reducing the types and number of sensors and facilitating the wear of sensor equipment.

[0030] Figure 2 The motion detection method according to the embodiment of the present disclosure is shown. Figure 2 As shown, the motion detection method includes:

[0031] Step S201: Acquire electromyographic signals of at least two detection positions of the detected object.

[0032] Step S202 , calling the motion detection model, processing the electromyographic signals of at least two detection positions, and obtaining joint angle information and muscle force information of at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position.

[0033] Step S203: determining a peak value of the electromyographic signal at each of the at least two detection positions.

[0034] In this embodiment, the execution subject may determine a peak value of the electromyographic signal at each of the at least two detection positions. Specifically, the peak value may refer to a peak value that is close in time between the at least two detection positions, that is, within a preset time range.

[0035] Step S204: determining the force application sequence between the various detection positions according to the peak values ​​of the electromyographic signals at the various detection positions.

[0036] In this embodiment, the execution entity may determine the time difference between the peak values ​​corresponding to at least two detection positions and determine the force application sequence based on the time difference. For example, the time difference may be input into a preset force application sequence formula to obtain the force application sequence output by the force application sequence formula.

[0037] Step S205 : On the target client, motion prompt information is generated based on the joint angle information, muscle strength information, and force generation sequence of at least two detection positions, and the motion prompt information is displayed.

[0038] In this embodiment, the execution subject may generate motion prompt information based on the joint angle information, muscle strength information, and force application sequence of at least two detection positions in a variety of ways. For example, the execution subject may determine the joint angle information, muscle strength information, and force application sequence of at least two detection positions as motion prompt information. Alternatively, the execution subject may input the joint angle information, muscle strength information, and force application sequence of at least two detection positions into a preset formula or model to obtain motion prompt information output from the model.

[0039] This embodiment can accurately determine the force generation sequence through the peak value of the electromyographic signal, thereby improving the comprehensiveness of the exercise prompt information.

[0040] In some optional implementations of this embodiment, electrical signal output devices are worn at at least two detection positions; the above method also includes: generating posture data of at least two detection positions based on joint angle information, muscle strength information and force generation sequence of at least two detection positions; among the at least two detection positions, determining the detection position whose posture data is to be corrected as the target stimulation position; transmitting an electrical signal output instruction to the electrical signal output device at the target stimulation position, so that the electrical signal output device at the target stimulation position outputs an electrical signal for electrical stimulation, so that the muscle passively generates force.

[0041] In these implementations, an electrical signal output device can be worn at at least two of the aforementioned stimulation locations on the subject being tested. This way, if the user's posture data at a detection location requires correction, the electrical signal output device at that location can output an electrical signal to the user. This electrical signal can be a voltage or a current. If it is a voltage, the voltage is less than or equal to a preset voltage threshold. If it is a current, the current is less than or equal to a preset current threshold. Specifically, the electrical signal output by the electrical signal output device can act on the skin, thereby stimulating the muscles beneath the skin.

[0042] The above-mentioned execution subject can adopt a variety of methods to generate posture data of at least two detection positions based on the joint angle information, muscle strength information and force sequence of at least two detection positions. For example, the above-mentioned execution subject can use the joint angle information, muscle strength information and force sequence of at least two detection positions as the posture data of at least two detection positions. Alternatively, the above-mentioned execution subject can call a preset posture generation model to process the joint angle information, muscle strength information and force sequence of at least two detection positions to generate posture data of at least two detection positions. The posture generation model can be, for example, a deep neural network.

[0043] The execution subject may use a variety of methods to determine, from among at least two detection positions, the detection position whose posture data is to be corrected as the target stimulation position. For example, the execution subject may input the posture data of at least two detection positions into a preset judgment model and obtain the target stimulation position outputted by the judgment model. The judgment model may determine whether the posture data of each detection position is accurate. If the posture data is not accurate, the detection position corresponding to the posture data is the detection position to be corrected, i.e., the target stimulation position.

[0044] These implementations can output electrical signals to prompt users of which detection position's posture data needs to be corrected, thereby helping users to make targeted corrections to the movements of different parts of their body during independent exercise.

[0045] In some optional application scenarios of these implementations, the above-mentioned determination of the detection position whose posture data needs to be corrected among at least two detection positions may include: comparing the posture data of at least two detection positions with the standard action posture model; and determining the detection position whose posture data is inconsistent with the standard action posture model among at least two detection positions as the detection position whose posture data needs to be corrected.

[0046] In these application scenarios, the execution entity can compare the posture data of the detected object with a standard motion posture model to obtain a comparison result. The standard motion posture model represents a standard motion posture, i.e., the posture data used as a reference. By comparing the posture data of the detected object with the standard motion posture model, the accuracy of the posture data at each detection position can be determined.

[0047] Optionally, the step of generating the above-mentioned standard motion posture model may include: collecting standard posture data of each motion posture at multiple preset detection positions through a sensor combination, wherein the sensor combination includes an electromyography sensor (sEMG sensor), an inertial sensor (IMU) and a mechanical sensor; training the deep learning model to be trained according to the standard posture data of each motion posture at multiple preset detection positions to obtain a standard motion posture model.

[0048] The execution entity or other electronic device can collect standard posture data of the target object at multiple preset detection positions through a sensor combination, wherein the sensor combination includes an electromyographic signal sensor (sEMG sensor), an inertial sensor (IMU), and a mechanical sensor. Taking the execution entity as an example, the execution entity can collect standard posture data and train a deep learning model based on this standard posture data to obtain a standard motion posture model, which facilitates comparison between the detected posture data and the standard motion posture model.

[0049] In these optional cases, the accuracy of the detected position to be corrected can be ensured by using a standard motion posture model. In addition, the standard posture data can be comprehensively and accurately collected through a combination of multiple sensors.

[0050] In some optional application scenarios of these implementations, obtaining electromyographic signals of at least two detection positions of the detected object includes: collecting electromyographic signals of at least two detection positions of the detected object by using an electromyographic signal sensor.

[0051] In these application scenarios, the electromyographic signals can be processed through the motion detection model so that inertial sensors and mechanical sensors can be omitted when collecting data, and only the electromyographic signal sensors can be used for signal collection, thereby saving collection costs and reducing the weight of the detected object.

[0052] In some optional application scenarios of these implementations, after transmitting an electrical signal output instruction to the electrical signal output device at the target stimulation position, the above method may further include: monitoring the muscle status of at least two detection positions; if among the muscle status of at least two detection positions, the muscle status of at least one detection position is a muscle fatigue state; stopping transmitting electrical signals to the muscles of at least one detection position.

[0053] In these application scenarios, muscle status can be monitored to avoid continued stimulation of a location when muscle fatigue caused by electrical signal stimulation has already set in. Once muscle fatigue occurs at a certain detection location, electrical signal transmission to that location can be stopped.

[0054] In this way, excessive signal transmission to the user can be avoided during the user's independent exercise, and the output of the electrical signal can be kept within a reasonable range.

[0055] like Figure 3 The figure shows a schematic diagram of an application scenario of the motion detection method disclosed herein. Detection location 1 can be set on a person's buttocks, and detection locations 2, 3, and 4 can be set on the person's legs. Sensors collect electromyographic signals at the detection locations and transmit them to a terminal held by the user via Bluetooth.

[0056] The present disclosure provides a motion detection device, which is used to perform the motion detection method of the above embodiment. Figure 4As shown, the device includes: an acquisition unit 401, configured to acquire electromyographic signals of at least two detection positions of the detected object; a calling unit 402, configured to call a motion detection model, process the electromyographic signals of at least two detection positions, and obtain joint angle information and muscle strength information of at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position; a generating unit 403, configured to generate and display motion prompt information on the target client according to the joint angle information and muscle strength information of at least two detection positions.

[0057] Optionally, the generation unit 403 is further configured to execute in the following manner on the target client, generating and displaying motion prompt information based on the joint angle information and muscle strength information of at least two detection positions: determining the peak value of the electromyographic signal of each of the at least two detection positions; determining the force application sequence between each detection position based on the peak value of the electromyographic signal of each detection position; generating motion prompt information and displaying the motion prompt information on the target client based on the joint angle information, muscle strength information and force application sequence of at least two detection positions.

[0058] Optionally, at least two detection positions are equipped with electrical signal output devices; the device may further include: a generation unit configured to generate posture data of at least two detection positions based on joint angle information, muscle strength information and force generation sequence of at least two detection positions; a determination unit configured to determine, among at least two detection positions, the detection position whose posture data is to be corrected as the target stimulation position; a transmission unit configured to transmit an electrical signal output instruction to the electrical signal output device of the target stimulation position so that the electrical signal output device of the target stimulation position outputs an electrical signal.

[0059] Optionally, the determination unit is further configured to determine the detection position whose posture data needs to be corrected in at least two detection positions in the following manner: comparing the posture data of at least two detection positions with the standard action posture model; and determining the detection position whose posture data is inconsistent with the standard action posture model among at least two detection positions as the detection position whose posture data needs to be corrected.

[0060] Optionally, the step of generating a standard motion posture model includes: collecting standard posture data of each motion posture at multiple preset detection positions through a sensor combination, wherein the sensor combination includes an electromyography signal sensor, an inertial sensor, and a mechanical sensor; training a deep learning model to be trained based on the standard posture data of each motion posture at multiple preset detection positions to obtain a standard motion posture model.

[0061] Optionally, the acquiring unit 401 is further configured to acquire the electromyographic signals of at least two detection positions of the detected object in the following manner: collecting the electromyographic signals of at least two detection positions of the detected object through an electromyographic signal sensor.

[0062] Optionally, the device also includes: after transmitting an electrical signal output instruction to the electrical signal output device of the target stimulation position, monitoring the muscle status of at least two detection positions; if among the muscle status of at least two detection positions, the muscle status of at least one detection position is a muscle fatigue state; stopping the transmission of electrical signals to the muscles of at least one detection position.

[0063] The motion detection device provided by the above-mentioned embodiment of the present disclosure and the motion detection method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0064] The present disclosure also provides an electronic device corresponding to the motion detection method provided in the above embodiment to perform the above motion detection method.

[0065] Please refer to Figure 5 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Figure 5 As shown, the electronic device 50 includes: a processor 500, a memory 501, a bus 502 and a communication interface 503. The processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; the memory 501 stores a computer program that can be run on the processor 500, and when the processor 500 runs the computer program, it executes the method provided in any of the aforementioned embodiments of the present disclosure.

[0066] The memory 501 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element and at least one other network element are connected via at least one communication interface 503 (which may be wired or wireless), and may use the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0067] Bus 502 may be an ISA bus, a PCI bus, or an EISA bus. Buses may be classified as address buses, data buses, and control buses. Memory 501 is used to store programs, and processor 500 executes the programs upon receiving execution instructions. The motion detection method disclosed in any of the aforementioned embodiments of the present disclosure may be applied to or implemented by processor 500.

[0068] The processor 500 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method may be completed by hardware integrated logic circuits or software instructions in the processor 500. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 501 , and the processor 500 reads the information in the memory 501 and completes the steps of the above method in combination with its hardware.

[0069] The electronic device provided by the embodiment of the present disclosure and the motion detection method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.

[0070] The present disclosure also provides a computer-readable storage medium corresponding to the motion detection method provided in the above embodiment. Figure 6 The computer-readable storage medium shown is an optical disc 60 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the motion detection method provided by any of the aforementioned embodiments is executed.

[0071] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0072] The computer-readable storage medium provided by the above-mentioned embodiment of the present disclosure and the motion detection method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0073] It should be noted that:

[0074] In the above text, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present disclosure is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present disclosure.

[0076] The embodiments of the present disclosure are described above in conjunction with the accompanying drawings, which are only specific implementation methods of the present disclosure. However, the present disclosure is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present disclosure, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present disclosure and the claims, which are all within the protection of the present disclosure.

Claims

1. A motion detection method, characterized in that: include: Acquiring electromyographic signals of at least two detection positions of the detected object; Invoking a motion detection model to process the electromyographic signals of the at least two detection positions to obtain joint angle information and muscle force information of the at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position; On the target client, motion prompt information is generated and displayed according to the joint angle information and muscle strength information of the at least two detection positions.

2. The method according to claim 1, characterized in that The target client generates and displays motion prompt information based on the joint angle information and muscle strength information of the at least two detection positions, including: determining a peak value of the electromyographic signal at each of the at least two detection positions; Determining a force application order between the detection positions according to peak values ​​of the electromyographic signals at the detection positions; On the target client, motion prompt information is generated based on the joint angle information, muscle strength information and the force generation sequence of the at least two detection positions, and the motion prompt information is displayed.

3. The method according to claim 2, characterized in that The at least two detection positions are provided with electrical signal output devices; The method further comprises: generating posture data of the at least two detection positions according to the joint angle information, muscle strength information, and the force generation sequence of the at least two detection positions; Among the at least two detection positions, determining the detection position whose posture data is to be corrected as the target stimulation position; An electrical signal output instruction is transmitted to the electrical signal output device at the target stimulation position, so that the electrical signal output device at the target stimulation position outputs an electrical signal.

4. The method according to claim 3, characterized in that Determining, among the at least two detection positions, a detection position at which posture data is to be corrected, includes: Comparing the posture data of the at least two detection positions with a standard action posture model; Among the at least two detection positions, the detection position where the comparison result of the posture data with the standard action posture model is inconsistent is determined as the detection position where the posture data is to be corrected.

5. The method according to claim 4, characterized in that The steps of generating the standard action posture model include: Collecting standard posture data of each motion posture at a plurality of preset detection positions through a sensor combination, wherein the sensor combination includes an electromyographic signal sensor, an inertial sensor, and a mechanical sensor; The deep learning model to be trained is trained according to the standard posture data of each motion posture at the multiple preset detection positions to obtain the standard motion posture model.

6. The method according to claim 3, characterized in that The step of obtaining electromyographic signals of at least two detection positions of the detected object includes: The myoelectric signal sensor collects myoelectric signals of at least two detection positions of the detected object.

7. The method according to claim 3, characterized in that After transmitting the electrical signal output instruction to the electrical signal output device at the target stimulation position, the method further includes: monitoring muscle states of the at least two detection locations; If the muscle state of at least one of the at least two detection positions is a muscle fatigue state; Transmission of electrical signals to the muscle at the at least one detection location is stopped.

8. A motion detection device, characterized in that: include: an acquisition unit configured to acquire myoelectric signals of at least two detection positions of the detected object; a calling unit configured to call a motion detection model, process the electromyographic signals of the at least two detection positions, and obtain joint angle information and muscle force information of the at least two detection positions, wherein the joint angle information of each detection position is used to indicate the angle of the joint closest to the detection position; The generating unit is configured to generate and display motion prompt information on the target client according to the joint angle information and muscle strength information of the at least two detection positions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor runs the computer program to implement the method according to any one of claims 1 to 7.

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

Images