A method and device for detecting the efficiency of a motion assistance device

Abstract

The application discloses a kind of motion power-assisted equipment power-assisted efficiency detection method and device, the method includes the muscle maximum effort state when receiving and the electromyographic data in the motion process;The received electromyographic data is processed to obtain MVC data and motion data;According to the MVC data and motion data, the power-assisted efficiency of motion power-assisted equipment based on electromyographic data is calculated;Respiration data is received and respiration data prediction model is established according to respiration data;Respiration data within a predetermined time is obtained by respiration data prediction model;The power-assisted efficiency of motion power-assisted equipment based on respiration data is calculated according to respiration data within a predetermined time;According to power-assisted efficiency, the comprehensive power-assisted efficiency of motion power-assisted equipment is calculated.The application calculates the comprehensive power-assisted efficiency of motion power-assisted equipment according to based on electromyographic data and respiration data, reduces the influence of power-assisted efficiency calculation interference factor on calculation result, and reduces respiration data acquisition and processing difficulty by predicting respiration data.

Description

Technical Field

[0001] The present invention relates to the technical field of detection of sports power-assisting equipment, and in particular to a method and device for detecting the power-assisting efficiency of sports power-assisting equipment. Background Art

[0002] Motion assist equipment is a wearable robot that can assist human movement. In order to evaluate the assisting effect of motion assist equipment on human movement and verify the coordination of human and machine movement, it is necessary to carry out motion test experiments and collect a variety of human physiological data, interaction force data and kinematic data.

[0003] Existing testing of exercise-assistance devices typically requires collecting and analyzing large amounts of data to ensure accurate results. This large volume and diverse data types present significant challenges in data processing and analysis. Collecting only small amounts of data for exercise-assistance device testing can also lead to issues with test results. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention aims to provide a method and device for detecting power-assistance efficiency that can effectively reduce the problem of large data processing volume in detecting power-assistance equipment while taking into account the detection accuracy of the power-assistance equipment.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides a method for detecting the power-assisting efficiency of a sports power-assisting device, comprising the following steps:

[0006] receiving first electromyographic data when the muscle is in a state of maximum force and second electromyographic data during movement;

[0007] Performing feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data;

[0008] Calculate the first power-assistance efficiency η of the exercise-assistance equipment based on electromyographic data according to the MVC data and the exercise data. EMG ;

[0009] receiving respiratory data and establishing a respiratory data prediction model based on the respiratory data;

[0010] Obtaining respiratory data within a predetermined time period through a respiratory data prediction model;

[0011] Calculating a second assist efficiency η of the exercise assisting device based on the breathing data according to the breathing data within a predetermined time breath ;

[0012] According to the first assist efficiency η EMG and the second power-assisting efficiency η breath Calculate the comprehensive power-assisting efficiency of sports power-assisting equipment.

[0013] Further, the first power-assistance efficiency η of the sports power-assistance equipment based on the electromyographic data is calculated according to the MVC data and the motion data. EMG include:

[0014] Calculating an average value of the electromyographic data during the movement according to the second electromyographic data during the movement;

[0015] Extracting the maximum value of the electromyographic data from the first electromyographic data when the muscle is in the maximum force state;

[0016] The power-assistance efficiency η of the sports power-assistance equipment on a single muscle is calculated according to the average value of the electromyographic data and the maximum value of the electromyographic data according to the first predetermined formula. i

[0017] The first power-assistance efficiency η is calculated according to the power-assistance efficiency of the sports power-assistance equipment on each muscle according to the second predetermined formula. EMG .

[0018] Furthermore, the first predetermined formula is:

[0019]

[0020] Wherein, avg(EMG) is the average value of the electromyographic data during the exercise process, and avg(EMG) is the maximum value of the electromyographic data in the first electromyographic data when the muscle is in the maximum force state;

[0021] The second predetermined formula is:

[0022]

[0023] Where n is the number of muscle blocks collected.

[0024] Furthermore, receiving the respiratory data and establishing a respiratory data prediction model based on the respiratory data includes:

[0025] Extract 5-dimensional feature data from respiratory data to form a feature sequence that changes over time; the 5-dimensional feature data includes respiratory rate (Rf), respiratory entropy (RQ), pulmonary ventilation (VE), oxygen consumption (VO2) and heart rate (HR);

[0026] Performing normalization processing on each column of data in the feature sequence according to a third predetermined formula to obtain an element value of each feature;

[0027] Use time sliding window technology to construct training sets and prediction sets;

[0028] A respiratory data prediction model is established based on the training set and the prediction set.

[0029] Furthermore, the respiratory data within a predetermined time period obtained by the respiratory data prediction model includes:

[0030] Input 5-dimensional respiratory data over a period of time into the respiratory data prediction model;

[0031] The respiratory data prediction model is continuously recursively used to predict the respiratory data within a predetermined time.

[0032] Further, the second power-assistance efficiency η of the exercise power-assistance equipment based on the breathing data is calculated according to the breathing data within the predetermined time. breath include:

[0033] Extracting oxygen consumption data of the person wearing the exercise assist device and oxygen consumption data of the person not wearing the exercise assist device from the respiratory data within a predetermined time;

[0034] The average oxygen consumption V1 of the whole process of wearing the sports power assist device and the average oxygen consumption V2 of the whole process of not wearing the sports power assist device are calculated based on the oxygen consumption data of wearing the sports power assist device and the oxygen consumption data of not wearing the sports power assist device;

[0035] According to the formula Calculate the second assist efficiency η breath ; Among them, V1 is the average oxygen consumption during the entire process of wearing sports assist equipment, V2 is the average oxygen consumption during the entire process of not wearing sports assist equipment, and V0 is the oxygen consumption in the resting state.

[0036] Further, according to the first power efficiency η EMG and the second power-assisting efficiency η breath Calculation of comprehensive power efficiency of sports power equipment includes:

[0037] According to the formula η=η EMG *a+η breath *b Calculate the comprehensive power efficiency of sports power equipment;

[0038] Wherein, a is the weight coefficient of the power assist efficiency based on electromyographic data, b is the weight coefficient of the power assist efficiency based on respiratory data, and a+b=1.

[0039] A second aspect of the present invention provides a device for detecting the power-assisting efficiency of a sports power-assisting device, comprising:

[0040] The first receiving module is used to receive the first myoelectric data when the muscle is in the maximum force state and the second myoelectric data during the movement;

[0041] A feature processing module, configured to perform feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data;

[0042] A first power-assistance efficiency calculation module, configured to calculate a first power-assistance efficiency of the sports power-assistance equipment based on electromyographic data according to the MVC data and the motion data;

[0043] a second receiving module, configured to receive respiratory data and establish a respiratory data prediction model based on the respiratory data;

[0044] A prediction module, configured to obtain respiratory data within a predetermined time period through a respiratory data prediction model;

[0045] A second power-assistance efficiency calculation module, configured to calculate a second power-assistance efficiency of the exercise power-assistance equipment based on the breathing data according to the breathing data within a predetermined time;

[0046] A calculation module is used to calculate the comprehensive power-assisting efficiency of the sports power-assisting equipment according to the first power-assisting efficiency and the second power-assisting efficiency.

[0047] A third aspect of the present invention provides an electronic device, characterized by comprising:

[0048] one or more processors; and

[0049] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for detecting the power-assisting efficiency of the exercise-assisting device as described in the first aspect.

[0050] A fourth aspect of the present invention provides a storage medium, characterized in that a program is stored on the storage medium, and the program can implement the method for detecting the power-assisting efficiency of the exercise-assisting device as described in the first aspect.

[0051] The present invention calculates the comprehensive power-assisting efficiency of the motion-assisting equipment based on the power-assisting efficiency of the motion-assisting equipment based on electromyographic data and the power-assisting efficiency of the motion-assisting equipment based on respiratory data, thereby reducing the influence of interference factors in the power-assisting efficiency calculation on the calculation results. Moreover, a large amount of respiratory data within a certain period of time can be predicted through a respiratory prediction model using only a small amount of respiratory data, thereby ensuring the amount of respiratory data used to calculate the power-assisting efficiency and reducing the difficulty of respiratory data collection and processing.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 This is a flow chart of a method for detecting power-assisting efficiency of a sports power-assisting device according to an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of a method for detecting power-assisting efficiency of a sports power-assisting device according to another embodiment of the present invention;

[0056] Figure 3 This is a structural block diagram of a device for detecting the power-assisting efficiency of a sports power-assisting device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0058] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, it will be appreciated by those skilled in the art that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0059] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0060] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0061] like Figure 1 As shown, the first aspect of the present invention provides a method for detecting the power-assisting efficiency of a sports power-assisting device, comprising the following steps:

[0062] Step S100: receiving first electromyographic data when the muscle is in a maximum force state and second electromyographic data during movement;

[0063] Step S110: performing feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data;

[0064] Step S120: Calculate the first power-assistance efficiency η of the sports power-assistance equipment based on the electromyographic data according to the MVC data and the sports data. EMG ;

[0065] Step S130: receiving respiratory data and establishing a respiratory data prediction model based on the respiratory data;

[0066] Step S140: obtaining respiratory data within a predetermined time period through a respiratory data prediction model;

[0067] Step S150: Calculating the second power-assistance efficiency η of the exercise-assistance equipment based on the breathing data according to the breathing data within a predetermined time breath ;

[0068] Step S160: According to the first power-assisting efficiency η EMG and the second power-assisting efficiency η breath Calculate the comprehensive power-assisting efficiency of sports power-assisting equipment.

[0069] The first EMG data at maximum muscle exertion and the second EMG data during exercise received in step S100 may be EMG data collected by an EMG sensor or may be experimental data uploaded in batches via the experimental data management module. The uploaded experimental data types include respiration, heart rate, EMG, plantar force, kinematics, and joint torque data.

[0070] like Figure 2 As shown, in one embodiment of the present invention, the feature processing of step S110 is specifically as follows: using a Buttock filter to perform 20-500 Hz band-pass filtering on the electromyographic data to remove noise, then performing full-wave rectification, and using a 10 Hz low-pass filter to extract the envelope signal to obtain motion data and MVC data.

[0071] In one embodiment of the present invention, step S120 further includes:

[0072] Calculate the average value avg(EMG) of the electromyographic data during the exercise according to the second electromyographic data during the exercise;

[0073] Extract the maximum value of the electromyographic data (EMG MVC );

[0074] The power-assistance efficiency η of the sports power-assistance equipment on a single muscle is calculated according to the average value of the electromyographic data and the maximum value of the electromyographic data according to the first predetermined formula. i ;

[0075] The first power-assistance efficiency η is calculated according to the power-assistance efficiency of the sports power-assistance equipment on each muscle according to the second predetermined formula. EMG .

[0076] Wherein, the first predetermined formula is:

[0077]

[0078] Wherein, avg(EMG) is the average value of the electromyographic data during the exercise process, and avg(EMG) is the maximum value of the electromyographic data in the first electromyographic data when the muscle is in the maximum force state;

[0079] The second predetermined formula is:

[0080]

[0081] Where n is the number of muscle blocks collected.

[0082] In one embodiment of the present invention, step S130 includes:

[0083] Extract 5-dimensional feature data from respiratory data to form a feature sequence that changes over time; the 5-dimensional feature data includes respiratory rate (Rf), respiratory entropy (RQ), pulmonary ventilation (VE), oxygen consumption (VO2) and heart rate (HR);

[0084] Performing normalization processing on each column of data in the feature sequence according to a third predetermined formula to obtain an element value of each feature;

[0085] The training and prediction sets are constructed using a time sliding window technique. The window width is 15 elements, and the step size is 5 elements. Therefore, the training features are a 15x5 feature matrix, with 15 consecutive elements per column, and 5 being the number of features. The 16th element is the predicted value. The training and prediction sets are constructed recursively.

[0086] A respiratory data prediction model was established based on the training set and the prediction set. The respiratory data prediction model is a three-layer recurrent neural network model. The input layer is a 15*5 feature matrix set (the training set), the hidden layer has 10 neurons, and the output layer is a 16*5 prediction matrix. The Log-Cosh algorithm is used as the loss function, and the specific formula is as follows:

[0087]

[0088] y——true value;

[0089] {a}——predicted value.

[0090] In one embodiment of the present invention, step S140 includes:

[0091] Input 5-dimensional respiratory data within a period of time into the respiratory data prediction model; for example, input 5-dimensional respiratory data for the previous 5 minutes.

[0092] The respiratory data prediction model is used to predict the respiratory data within a predetermined time period through continuous recursion, and the output prediction value is added to the input set. After continuous recursion, 15 minutes of respiratory data are obtained to form a 20-minute respiratory data sequence.

[0093] In one embodiment of the present invention, step S150 includes:

[0094] Extracting oxygen consumption data of the person wearing the exercise assist device and oxygen consumption data of the person not wearing the exercise assist device from the respiratory data within a predetermined time;

[0095] The average oxygen consumption V1 of the whole process of wearing the sports power assist device and the average oxygen consumption V2 of the whole process of not wearing the sports power assist device are calculated based on the oxygen consumption data of wearing the sports power assist device and the oxygen consumption data of not wearing the sports power assist device;

[0096] According to the formula Calculate the second assist efficiency η breath ; Among them, V1 is the average oxygen consumption during the entire process of wearing sports assist equipment, V2 is the average oxygen consumption during the entire process of not wearing sports assist equipment, and V0 is the oxygen consumption in the resting state.

[0097] In one embodiment of the present invention, step S160 includes:

[0098] According to the formula η=η EMG *a+η breath *b Calculate the comprehensive power efficiency of sports power equipment;

[0099] Wherein, a is the weight coefficient of the power assist efficiency based on electromyographic data, b is the weight coefficient of the power assist efficiency based on respiratory data, and a+b=1.

[0100] A second aspect of the present invention provides a device for detecting the power-assistance efficiency of a sports power-assistance device, comprising a data analysis module, the data analysis module comprising:

[0101] The first receiving module is used to receive the first myoelectric data when the muscle is in the maximum force state and the second myoelectric data during the movement;

[0102] A feature processing module, configured to perform feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data;

[0103] A first power-assistance efficiency calculation module, configured to calculate a first power-assistance efficiency of the sports power-assistance equipment based on electromyographic data according to the MVC data and the motion data;

[0104] a second receiving module, configured to receive respiratory data and establish a respiratory data prediction model based on the respiratory data;

[0105] A prediction module, configured to obtain respiratory data within a predetermined time period through a respiratory data prediction model;

[0106] A second power-assistance efficiency calculation module, configured to calculate a second power-assistance efficiency of the exercise power-assistance equipment based on the breathing data according to the breathing data within a predetermined time;

[0107] A calculation module is used to calculate the comprehensive power-assisting efficiency of the sports power-assisting equipment according to the first power-assisting efficiency and the second power-assisting efficiency.

[0108] In one embodiment of the present invention, the power assist efficiency detection device further includes a login module, an experimental data management module, and an experimental condition management module.

[0109] The login module is used for account and password login, and can also set administrator permissions.

[0110] The experimental data management module is used to batch upload experimental data and set experimental conditions. Uploaded experimental data types include respiration, heart rate, electromyography, plantar force, kinematics, and joint torque data. Configurable experimental conditions include experimental time, motion assistance equipment model, experimenter, experimental conditions, and experimental equipment. Experimental data modification includes modifying experimental data types and experimental conditions. Experimental data query can query historical experimental data by experimental time, motion assistance equipment model, experimenter, experimental conditions, and experimental equipment.

[0111] The experimental conditions management module is used to record information about the experimenter, motion assistance equipment, and experimental equipment. Experimenter information includes gender, height, sex, age, joint dimensions, and ID number. Motion assistance equipment information includes model, production date, assistance mode, component modules, joint degrees of freedom, structural dimensions, and sensor distribution. Experimental equipment information includes the type of experimental equipment, operating mode, and terrain conditions.

[0112] The data analysis module is used for experimental data display and indicator extraction. The experimental data display function can plot data curves for respiration, heart rate, electromyography, plantar force, kinematics, and joint torque. The indicator extraction function includes extracting muscle activation and median frequency from electromyography experimental data, and comprehensively analyzing the power efficiency of sports power assistance equipment using respiratory data and electromyography data.

[0113] A third aspect of the present invention provides an electronic device, comprising:

[0114] one or more processors; and

[0115] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for detecting the power-assisting efficiency of the exercise-assisting device as described in the first aspect.

[0116] A fourth aspect of the present invention provides a storage medium, characterized in that a program is stored on the storage medium, and the program can implement the method for detecting the power-assisting efficiency of the sports power-assisting device as described in the first aspect. The computer-readable medium can be included in the electronic device described in the above embodiment; or it can exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the power-assisting efficiency detection method as described in the above embodiment.

[0117] In summary, the present invention calculates the comprehensive assistance efficiency of the sports assisting equipment based on the assistance efficiency of the sports assisting equipment based on the electromyographic data and the assistance efficiency of the sports assisting equipment based on the respiratory data, thereby reducing the influence of interference factors in the assistance efficiency calculation on the calculation results, and can use only a small amount of respiratory data through the respiratory prediction model to predict a large amount of respiratory data within a certain period of time, thereby ensuring the amount of respiratory data used to calculate the assistance efficiency and reducing the difficulty of respiratory data collection and processing.

[0118] According to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-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 a network via a communication portion, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present application are executed.

[0119] It should be noted that the computer-readable medium described in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying 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 thereof. 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.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from 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 or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The modules described in the embodiments of the present invention may be implemented in software or hardware, and the modules may be provided in a processor. In some cases, the names of these modules do not limit the modules themselves.

[0122] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0123] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0124] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0125] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for detecting the power-assistance efficiency of a sports power-assistance device, characterized in that: The steps include: receiving first electromyographic data when the muscle is in a state of maximum force and second electromyographic data during movement; Performing feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data; Calculate the first power-assistance efficiency η of the exercise-assistance equipment based on electromyographic data according to the MVC data and the exercise data. EMG ; receiving respiratory data and establishing a respiratory data prediction model based on the respiratory data; Obtaining respiratory data within a predetermined time period through a respiratory data prediction model; Calculating a second assist efficiency η of the exercise assisting device based on the breathing data according to the breathing data within a predetermined time breath ; According to the first assist efficiency η EMG and the second power-assisting efficiency η breath Calculate the comprehensive power-assisting efficiency of sports power-assisting equipment; Receiving respiratory data and establishing a respiratory data prediction model based on the respiratory data includes: Extract 5-dimensional feature data from respiratory data to form a feature sequence that changes over time; the 5-dimensional feature data includes respiratory rate (Rf), respiratory entropy (RQ), pulmonary ventilation (VE), oxygen consumption (VO2) and heart rate (HR); Performing normalization processing on each column of data in the feature sequence according to a third predetermined formula to obtain an element value of each feature; Use time sliding window technology to construct training sets and prediction sets; Establish a respiratory data prediction model based on the training set and the prediction set; The respiratory data within a predetermined time period obtained through the respiratory data prediction model includes: Input 5-dimensional respiratory data over a period of time into the respiratory data prediction model; The respiratory data prediction model is used to predict the respiratory data within a predetermined time period through continuous recursion. Calculating a second assist efficiency η of the exercise assisting device based on the breathing data according to the breathing data within a predetermined time breath include: Extracting oxygen consumption data of the person wearing the exercise assist device and oxygen consumption data of the person not wearing the exercise assist device from the respiratory data within a predetermined time; The average oxygen consumption V1 of the whole process of wearing the sports power assist device and the average oxygen consumption V2 of the whole process of not wearing the sports power assist device are calculated based on the oxygen consumption data of wearing the sports power assist device and the oxygen consumption data of not wearing the sports power assist device; According to the formula Calculate the second assist efficiency η breath ; Among them, V1 is the average oxygen consumption during the entire process of wearing sports assist equipment, V2 is the average oxygen consumption during the entire process of not wearing sports assist equipment, and V0 is the oxygen consumption in the resting state.

2. The method for detecting the power-assisting efficiency of a sports power-assisting device according to claim 1, wherein: Calculate the first power-assistance efficiency η of the exercise-assistance equipment based on electromyographic data according to the MVC data and the exercise data. EMG include: Calculating an average value of the electromyographic data during the movement according to the second electromyographic data during the movement; Extracting the maximum value of the electromyographic data from the first electromyographic data when the muscle is in the maximum force state; The power-assistance efficiency η of the sports power-assistance equipment on a single muscle is calculated according to the average value of the electromyographic data and the maximum value of the electromyographic data according to the first predetermined formula. i The first power-assistance efficiency η is calculated according to the power-assistance efficiency of the sports power-assistance equipment on each muscle according to the second predetermined formula. EMG .

3. The method for detecting the power-assisting efficiency of a power-assisting device according to claim 2, wherein: The first predetermined formula is: Wherein, avg(EMG) is the average value of the electromyographic data during the exercise process, and avg(EMG) is the maximum value of the electromyographic data in the first electromyographic data when the muscle is in the maximum force state; The second predetermined formula is: Where n is the number of muscle blocks collected.

4. The method for detecting the power-assisting efficiency of a sports power-assisting device according to claim 1, wherein: According to the first assist efficiency η EMG and the second power-assisting efficiency η breath Calculation of comprehensive power efficiency of sports power equipment includes: According to the formula η=η EMG *a+η breath *b Calculate the comprehensive power efficiency of sports power equipment; Wherein, a is the weight coefficient of the power assist efficiency based on electromyographic data, b is the weight coefficient of the power assist efficiency based on respiratory data, and a+b=1.

5. A device for detecting the power-assistance efficiency of a sports power-assistance device, characterized in that: include: The first receiving module is used to receive the first myoelectric data when the muscle is in the maximum force state and the second myoelectric data during the movement; A feature processing module, configured to perform feature processing on the received first electromyographic data and the second electromyographic data to obtain MVC data and motion data; A first power-assistance efficiency calculation module, configured to calculate a first power-assistance efficiency of the sports power-assistance equipment based on electromyographic data according to the MVC data and the motion data; a second receiving module, configured to receive respiratory data and establish a respiratory data prediction model based on the respiratory data; wherein receiving the respiratory data and establishing the respiratory data prediction model based on the respiratory data includes extracting five-dimensional feature data from the respiratory data to form a feature sequence that varies over time; wherein the five-dimensional feature data includes respiratory rate (Rf), respiratory entropy (RQ), pulmonary ventilation (VE), oxygen consumption (VO2), and heart rate (HR); performing normalization processing on each column of data in the feature sequence according to a third predetermined formula to obtain an element value of each feature; constructing a training set and a prediction set using a time sliding window technique; and establishing the respiratory data prediction model based on the training set and the prediction set; A prediction module is configured to obtain respiratory data within a predetermined time period using a respiratory data prediction model; wherein obtaining respiratory data within a predetermined time period using the respiratory data prediction model includes inputting 5-dimensional respiratory data within a period of time into the respiratory data prediction model; and predicting respiratory data within a predetermined time period by continuously recursively applying the respiratory data prediction model. The second power-assistance efficiency calculation module is used to calculate the second power-assistance efficiency of the motion-assistance equipment based on the respiratory data according to the respiratory data within a predetermined time; wherein the second power-assistance efficiency η of the motion-assistance equipment based on the respiratory data is calculated according to the respiratory data within a predetermined time. breath The method includes extracting oxygen consumption data of wearing sports power-assisting equipment and oxygen consumption data of not wearing sports power-assisting equipment from respiratory data within a predetermined time; calculating the average oxygen consumption V1 of the whole process of wearing sports power-assisting equipment and the average oxygen consumption V2 of the whole process of not wearing sports power-assisting equipment according to the oxygen consumption data of wearing sports power-assisting equipment and the oxygen consumption data of not wearing sports power-assisting equipment; and calculating the average oxygen consumption V1 of the whole process of wearing sports power-assisting equipment and the average oxygen consumption V2 of the whole process of not wearing sports power-assisting equipment according to the formula Calculate the second assist efficiency η breath ; Among them, V1 is the average oxygen consumption during the whole process of wearing sports power equipment, V2 is the average oxygen consumption during the whole process of not wearing sports power equipment, and V0 is the oxygen consumption in the resting state; A calculation module is used to calculate the comprehensive power-assisting efficiency of the sports power-assisting equipment according to the first power-assisting efficiency and the second power-assisting efficiency.

6. An electronic device, characterized in that: include: one or more processors; as well as A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for detecting the power-assisting efficiency of the sports power-assisting device as described in any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium stores a program, which can implement the method for detecting the power-assisting efficiency of the sports power-assisting device as described in any one of claims 1 to 4.

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