Muscle force prediction method and device based on FMG and sEMG signals

Abstract

The invention relates to the technical field of muscle activation state evaluation, in particular to a muscle force prediction method and device based on FMG and sEMG signals, and the method comprises the steps: collecting a muscle pressure signal generated when the muscle of a to-be-tested person moves based on an FMG technology; based on the sEMG technology, muscle physiological electric signals generated during muscle movement of the to-be-tested person are collected; target time domain features and target frequency domain features of the muscle pressure signals and the muscle physiological electric signals are extracted, standardization processing is carried out according to the target muscle strength level to obtain processed features, the processed features are input into a target machine learning model, and a muscle strength prediction result of the to-be-tested person is output. Therefore, the problems that in the prior art, sEMG signals are prone to noise interference, a high-density array needs to be used for signal analysis, the number of sensors is increased, the signal processing difficulty is increased, and the robustness of muscle force prediction is reduced are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of muscle activation state evaluation, and in particular to a muscle force prediction method and device based on FMG (ForceMyography, muscle pressure map) and sEMG (Surface Electromyography, surface electromyography) signals. Background Art

[0002] Human muscle function and activation status is an important physiological parameter indicator that reflects human athletic ability and health status. In the fields of sports science and clinical postoperative rehabilitation, the evaluation of muscle function and activation status is of great significance. For example, unbalanced muscle force during exercise can lead to joint instability, wear and tear, and even injury. In postoperative rehabilitation, muscle damage and insufficient muscle activation may lead to limited joint mobility, decreased joint strength, joint damage and pain. Therefore, being able to evaluate muscle activation status and function is of great value to human sports health.

[0003] Currently, the commonly used methods for quantifying muscle strength and muscle function mainly include imaging methods and dynamometers. Among them, imaging methods mainly include musculoskeletal ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), etc., which characterize the muscle structure characteristics through imaging features, and further infer muscle strength. The main problem is that it relies on experienced doctors, the measurement repeatability is poor, and it is difficult to monitor the muscles in real time during exercise; dynamometers include isokinetic dynamometers and dynamometers. Isokinetic dynamometers are large and expensive, and are not widely used. The measurement scenarios applicable to dynamometers are relatively limited, and they are also more dependent on doctors' measurement experience and standardized measurement methods, making them difficult to promote.

[0004] In the postoperative rehabilitation of musculoskeletal injuries, patients usually need long-term rehabilitation training. Real-time monitoring of muscle activation status through wearable devices in daily training can quantify the rehabilitation training effect of specific muscles. For example, sEMG is a common muscle physiological information. At present, there are some methods based on sEMG signals and machine learning models to predict muscle strength, thereby providing effective information for the formulation of rehabilitation training programs.

[0005] However, in the related technology, since sEMG signals are easily interfered by noise and sEMG is usually mixed with signals of deep muscles and superficial muscles, high-density arrays are required for signal analysis, which increases the number of sensors and the difficulty of signal processing, reducing the robustness of muscle strength prediction, which urgently needs to be solved. Summary of the invention

[0006] The present invention provides a muscle strength prediction method and device based on FMG and sEMG signals to solve the problem in the related art that sEMG signals are easily interfered by noise and sEMG is usually mixed with signals of deep muscles and surface muscles, so a high-density array needs to be used for signal analysis, which increases the number of sensors and the difficulty of signal processing, thereby reducing the robustness of muscle strength prediction.

[0007] The first aspect of the present invention provides a muscle strength prediction method based on FMG and sEMG signals, comprising the following steps: based on the muscle pressure map FMG technology, collecting the muscle pressure signal generated by the muscle activity of at least one person to be tested; based on the surface electromyography sEMG technology, collecting the muscle physiological electrical signal generated by the muscle activity of the at least one person to be tested; extracting the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal, and standardizing the target time domain features and the target frequency domain features according to the target muscle strength level to obtain the processed features, and inputting the processed features into the target machine learning model to output the muscle strength prediction result of the at least one person to be tested.

[0008] Optionally, in one embodiment of the present invention, the extraction of target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal includes: extracting a first average value feature based on a target active segment and a second average value feature based on an inactive segment of the muscle pressure signal; extracting a root mean square feature, a mean absolute value feature, a slope sign change rate feature, a zero crossing rate feature, a median frequency feature and an average power frequency feature of the muscle physiological electrical signal.

[0009] Optionally, in one embodiment of the present invention, the target time domain features and the target frequency domain features are standardized according to the target muscle strength level to obtain processed features, including: extracting signal features of each muscle of the at least one person to be tested under the target action type; and performing signal normalization processing on the signal features of the at least one person to be tested based on the training label weight range to obtain the processed features.

[0010] Optionally, in one embodiment of the present invention, before inputting the processed features into the target machine learning model, it also includes: using the processed features to determine a feature data set, and dividing the feature data set into a training set and a test set; using the training set to train a pre-built machine learning model to obtain a trained machine learning model; inputting the test set into the trained machine learning model to output a model prediction result, and generating the target machine learning model when the model prediction result meets a preset test condition.

[0011] The second aspect of the present invention provides a muscle strength prediction device based on FMG and sEMG signals, including: a first acquisition module, used to collect muscle pressure signals generated by muscle activity of at least one person to be tested based on muscle pressure map FMG technology; a second acquisition module, used to collect muscle physiological electrical signals generated by muscle activity of at least one person to be tested based on surface electromyography sEMG technology; a prediction module, used to extract target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal, and standardize the target time domain features and the target frequency domain features according to the target muscle strength level to obtain processed features, and input the processed features into a target machine learning model to output the muscle strength prediction result of the at least one person to be tested.

[0012] Optionally, in one embodiment of the present invention, the prediction module includes: a first extraction unit, used to extract a first average value feature of the muscle pressure signal based on the target active segment and a second average value feature based on the inactive segment; a second extraction unit, used to extract a root mean square feature, a mean absolute value feature, a slope sign change rate feature, a zero crossing rate feature, a median frequency feature and an average power frequency feature of the muscle physiological electrical signal.

[0013] Optionally, in one embodiment of the present invention, the prediction module includes: a third extraction unit, used to extract the signal characteristics of each muscle of the at least one person to be tested under the target action type; and a processing unit, used to perform signal normalization processing on the signal characteristics of the at least one person to be tested based on a training label weight range to obtain the processed characteristics.

[0014] Optionally, in one embodiment of the present invention, the device of the embodiment of the present invention also includes: a determination module, which is used to determine a feature data set using the processed features before inputting the processed features into the target machine learning model, and divide the feature data set into a training set and a test set; an acquisition module, which is used to train a pre-built machine learning model using the training set before inputting the processed features into the target machine learning model to obtain a trained machine learning model; and a generation module, which is used to input the test set into the trained machine learning model before inputting the processed features into the target machine learning model to output a model prediction result, and generate the target machine learning model when the model prediction result meets a preset test condition.

[0015] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the muscle strength prediction method based on FMG and sEMG signals as described in the above embodiment.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned muscle strength prediction method based on FMG and sEMG signals.

[0017] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned muscle strength prediction method based on FMG and sEMG signals.

[0018] The embodiment of the present invention can collect the muscle pressure signal generated by the muscle activity of the person to be tested based on the FMG technology, and collect the muscle physiological electrical signal generated by the muscle activity of the person to be tested based on the sEMG technology, then extract the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal, and perform standardized processing according to the target muscle strength level to obtain the processed features, and input the processed features into the target machine learning model to output the muscle strength prediction result of the person to be tested, effectively reducing the cost requirements and improving the robustness of muscle strength prediction. Thus, the problem in the related art that the sEMG signal is easily interfered by noise and needs to use a high-density array for signal analysis, which increases the number of sensors, increases the difficulty of signal processing, and reduces the robustness of muscle strength prediction is solved.

[0019] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of the structure of a muscle strength prediction system based on FMG and sEMG signals provided according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the arrangement of an FMG sensor and a sEMG sensor according to a specific embodiment of the present invention; Figure 3 A flowchart of a muscle strength prediction method based on FMG and sEMG signals provided according to an embodiment of the present invention; Figure 4It is a schematic diagram of a 90-degree isometric contraction action of curling and stretching according to a specific embodiment of the present invention; Figure 5 It is a flowchart of a muscle strength prediction method based on FMG and sEMG signals according to a specific embodiment of the present invention; Figure 6 A schematic diagram of the original data set normalization and model training process according to a specific embodiment of the present invention; Figure 7 A schematic diagram showing comparison of model results of three different signal strategies according to a specific embodiment of the present invention; Figure 8 A schematic diagram of the structure of a muscle strength prediction device based on FMG and sEMG signals provided according to an embodiment of the present invention; Fig. 9 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0022] The following describes the muscle strength prediction method and device based on FMG and sEMG signals of the embodiment of the present invention with reference to the accompanying drawings. In view of the problem that the sEMG signal is easily interfered by noise and needs to use a high-density array for signal analysis in the related technology mentioned in the background technology center, the number of sensors is increased, the difficulty of signal processing is increased, and the robustness of muscle strength prediction is reduced, the present invention provides a muscle strength prediction method based on FMG and sEMG signals, in which the muscle pressure signal generated by the muscle activity of the person to be tested can be collected based on the FMG technology, and the muscle physiological electrical signal generated by the muscle activity of the person to be tested can be collected based on the sEMG technology, then, the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, and standardized according to the target muscle strength level to obtain the processed features, and the processed features are input into the target machine learning model to output the muscle strength prediction result of the person to be tested, effectively reducing the cost requirements and improving the robustness of muscle strength prediction. Thereby, the problem in the related art that sEMG signals are easily affected by noise and require the use of high-density arrays for signal analysis, which increases the number of sensors, increases the difficulty of signal processing, and reduces the robustness of muscle strength prediction is solved.

[0023] like Figure 1As shown, an embodiment of the present invention designs a muscle strength prediction system based on FMG and sEMG signals, including a sensor module and a signal acquisition circuit.

[0024] Among them, Figure 2 As shown, based on the sensor module and the signal acquisition circuit, an embodiment of the present invention designs a wearable device for collecting FMG signals and sEMG signals, wherein the FMG sensor and the sEMG sensor synchronously and co-locately collect muscle pressure signals and muscle physiological electrical signals. The sEMG sensor is placed at the belly of the muscle to be measured. Taking the measurement of elbow joint muscle strength as an example, the sEMG sensor is placed at three positions: the biceps, the long head of the triceps, and the lateral head of the triceps. The sEMG electrode is attached to the skin surface through gel to collect sEMG signals, i.e., muscle physiological electrical signals. The FMG sensor is set on a circle of straps on the outside to collect FMG signals, i.e., muscle pressure signals.

[0025] Among them, the FMG signal is the pressure signal of the body surface-strap interface caused by muscle activity under the external strap. Its signal acquisition is highly robust and not easily interfered by noise. In addition, the FMG signal is highly correlated with muscle strength. The fusion of FMG and sEMG signals can reduce the number of sensors, improve system robustness and recognition accuracy, and is simple to operate and easy to promote, and can continuously monitor specific muscles for a long time.

[0026] Specifically, Figure 3 A schematic flow chart of a muscle strength prediction method based on FMG and sEMG signals provided in an embodiment of the present invention.

[0027] like Figure 3 As shown, the muscle strength prediction method based on FMG and sEMG signals includes the following steps: In step S301, based on the muscle pressure graph (FMG) technology, muscle pressure signals generated during muscle activity of at least one person to be tested are collected.

[0028] It is understandable that the embodiment of the present invention can utilize the FMG technology in the above-mentioned wearable device to collect the muscle pressure signal generated by the muscle activity of at least one person to be tested. For example, Figure 4 As shown in the figure, a muscle strength test experimental method is used. The subject performs a relaxation-90 degree isometric contraction cycle under the voice command of the computer software. The active segment of the signal is divided according to the voice command timestamp. The signals of the active segment and the relaxation segment will be used for subsequent feature extraction and normalization, which effectively improves the feasibility of muscle strength prediction.

[0029] Among them, the signal acquisition circuit in the above device can collect and transmit the sensor signal, and can generate a 1kHz sine wave to provide a power supply signal to the FMG sensor; the impedance of the FMG sensor is converted into voltage, and the effective value is calculated as the FMG signal, that is, the muscle pressure signal.

[0030] In step S302, based on the surface electromyography (sEMG) technology, muscle physiological electrical signals generated by muscle activity of at least one person to be tested are collected.

[0031] It can be understood that the embodiments of the present invention can utilize the sEMG technology in the above-mentioned wearable device to collect muscle physiological electrical signals generated by muscle activity of at least one person to be tested. For example, in the above-mentioned muscle strength test experiment, the signal acquisition circuit in the above-mentioned device can filter (10-500Hz bandpass filtering, human common mode noise suppression), amplify and collect the sEMG signal, that is, the muscle physiological electrical signal, and transmit the sEMG signal to the host computer software, thereby effectively improving the reliability of muscle strength prediction.

[0032] Among them, since the FMG sensing power supply signal produces crosstalk on the sEMG signal, based on the above signal acquisition circuit, the sEMG acquisition frequency is set to 1600Hz. At this time, the 1kHz FMG AC power supply signal causes 600Hz noise to the sEMG, which can be filtered out by the bandpass filter.

[0033] In step S303, the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, and the target time domain features and target frequency domain features are standardized according to the target muscle strength level to obtain processed features, and the processed features are input into the target machine learning model to output the muscle strength prediction result of at least one person to be tested.

[0034] In an embodiment of the present invention, the target machine learning model is a pre-trained optimal machine learning model, which will be introduced in the following steps and will not be elaborated here.

[0035] It can be understood that the embodiments of the present invention can extract the target time domain features and target frequency domain features of the muscle pressure signal and muscle physiological electrical signal of each test subject, and standardize the target time domain features and target frequency domain features of each test subject according to the muscle strength level, such as performing normalization processing to obtain processed features, and can also uniformly normalize the personal information (height, weight, age, gender, etc.) of all test subjects, and input the processed features and the personal information of the test subjects into the target machine learning model, such as a trained SVR regression model, so that the muscle strength prediction results of each test subject can be output, effectively reducing cost requirements and improving the accuracy of muscle strength prediction.

[0036] Among them, in one embodiment of the present invention, the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, including: extracting the first average value feature of the muscle pressure signal based on the target active segment and the second average value feature based on the inactive segment; extracting the root mean square feature, mean absolute value feature, slope sign change rate feature, zero crossing rate feature, median frequency feature and average power frequency feature of the muscle physiological electrical signal.

[0037] In the actual implementation process, the embodiment of the present invention can extract the average value of the active segment of the FMG signal, that is, the muscle pressure signal as the signal feature when the muscle is activated, and extract the average value of the inactive segment as the initial pressure value; extract the root mean square, average absolute value, slope sign change rate, zero crossing rate, median frequency, average power frequency and other characteristics of the sEMG signal, that is, the muscle physiological electrical signal, among which the root mean square can represent the overall intensity of muscle activity; the average absolute value can also represent the overall intensity of muscle activity, but is suitable for situations where the influence of noise needs to be reduced; the slope sign change rate can be used to identify muscle fatigue or different types of exercise; the zero crossing rate can evaluate the dynamic properties of muscle activity; the median frequency can be used to monitor muscle fatigue status; the average power frequency can also be used to evaluate the degree of muscle fatigue, so as to deeply understand the nature of muscle activity, improve sports performance, and promote rehabilitation.

[0038] Optionally, in one embodiment of the present invention, the target time domain features and the target frequency domain features are standardized according to the target muscle strength level to obtain processed features, including: extracting signal features of each muscle of at least one person to be tested under the target action type; and performing signal normalization processing on the signal features of at least one person to be tested based on the training label weight range to obtain the processed features.

[0039] As a possible implementation method, the embodiment of the present invention can extract the signal features of each muscle of each person to be tested under the target action type, for example, when the person to be tested performs isometric contraction movements of curling and extending their arms 90 degrees, and based on the training label weight range, for example, perform signal normalization processing on the signal features of each person to be tested according to the muscle strength level range of 0kg, 1kg, 2kg and 3kg to obtain the processed features, thereby improving the generalization ability and prediction accuracy of subsequent models.

[0040] Optionally, in one embodiment of the present invention, before inputting the processed features into the target machine learning model, it also includes: using the processed features to determine a feature data set, and dividing the feature data set into a training set and a test set; using the training set to train a pre-built machine learning model to obtain a trained machine learning model; inputting the test set into the trained machine learning model to output the model prediction results, and generating a target machine learning model when the model prediction results meet preset test conditions.

[0041] In the embodiment of the present invention, combined with Figure 5 and Figure 6 As shown, first, a signal feature standardization method can be designed. Under a specific action type, each data set contains data from multiple subjects. The personal information (height, weight, age, gender, etc.) of each subject is recorded. The features of the signal of each muscle are extracted, and the muscle strength level corresponding to each row of data is stored in the data label column to obtain the original data set. Data standardization is a routine operation in machine learning, but the traditional method standardizes the same features of all people together. Different from this traditional method, the embodiment of the present invention can normalize the signal features of each person according to the training label weight range. For example, the data within the 0-3kg label range of each subject is standardized, thereby eliminating the signal baseline differences caused by individual differences, so that the model output results are greatly improved. For the personal information of the subjects, since each person has only one piece of information, this part of the data is uniformly normalized in a traditional standardization manner.

[0042] Furthermore, combined with Figure 5 and Figure 6As shown, the embodiment of the present invention can obtain a feature data set after the above standardization, and divide the feature data set to obtain a training set and a test set, wherein the data of the training set is used to train the model, and the test set is used to evaluate the model effect. The data set division method may include random division; the first 80% is used for the training set, the last 20% is used for the test set; n-1 subjects are used for the training set, and the remaining 1 subject is used for the test set. After training the model with the training set data, the optimal parameters can be obtained, and these parameters are saved and then used for prediction by the model. Taking a pre-built machine learning model, such as an SVR model, as an example, its model training and parameter optimization process is: searching for the best kernel function type, gamma and C through grid parameters, the kernel function type may include linear, rbf, poly, etc., and the search range of gamma and C is 0.001 to 1000. The training features of the input model include the above-mentioned standardized signal features, as well as the height, weight, age, gender and other features of the subject to obtain a trained SVR model. Next, in the model evaluation phase, the data of the test set is input to obtain the model prediction results. The model prediction results can be compared with the labels of the test set to evaluate the quality of the model. When the model evaluation result is optimal, the target machine learning model is generated. Secondly, when the model is deployed and applied, the standardized data (personal information and signal feature data of each person to be tested) is input into the trained model to obtain the prediction results.

[0043] It should be noted that the preset test conditions are conditions under which the model evaluation results are optimal, which are specifically set by those skilled in the art and are not specifically limited here.

[0044] For example, Figure 7 As shown in the figure, the model evaluation results are compared based on the data set partitioning method of "n-1 subjects are used for the training set and the remaining 1 subject is used for the test set", where each point represents the score value output by the model when the data of a certain subject is used as the test set. It can be seen that compared with a single FMG or sEMG signal, the iFEMG signal fusion strategy can improve the situation where the model fitting fails for some subjects, so that the model has a higher score value for all subjects, which further indicates that the iFEMG strategy can improve the accuracy and versatility of the model.

[0045] According to the muscle strength prediction method based on FMG and sEMG signals proposed in the embodiment of the present invention, the muscle pressure signal generated by the muscle activity of the person to be tested can be collected based on the FMG technology, and the muscle physiological electrical signal generated by the muscle activity of the person to be tested can be collected based on the sEMG technology. Then, the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, and standardized according to the target muscle strength level to obtain the processed features, and the processed features are input into the target machine learning model to output the muscle strength prediction result of the person to be tested, effectively reducing the cost requirements and improving the robustness of muscle strength prediction. Thus, the problem in the related art that the sEMG signal is easily interfered by noise and needs to use a high-density array for signal analysis, which increases the number of sensors, increases the difficulty of signal processing, and reduces the robustness of muscle strength prediction is solved.

[0046] Next, a muscle strength prediction device based on FMG and sEMG signals according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0047] Figure 8 It is a block diagram of a muscle strength prediction device based on FMG and sEMG signals according to an embodiment of the present invention.

[0048] like Figure 8 As shown, the muscle strength prediction device 10 based on FMG and sEMG signals includes: a first acquisition module 100 , a second acquisition module 200 and a prediction module 300 .

[0049] Specifically, the first acquisition module 100 is used to acquire muscle pressure signals generated during muscle activity of at least one person to be tested based on the muscle pressure graph FMG technology.

[0050] The second acquisition module 200 is used to acquire muscle physiological electrical signals generated by muscle activity of at least one person to be tested based on surface electromyography (sEMG) technology.

[0051] The prediction module 300 is used to extract the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal, and standardize the target time domain features and target frequency domain features according to the target muscle strength level to obtain processed features, and input the processed features into the target machine learning model to output the muscle strength prediction result of at least one person to be tested.

[0052] Optionally, in one embodiment of the present invention, the prediction module 300 includes: a first extraction unit and a second extraction unit.

[0053] The first extraction unit is used to extract a first average value feature of the muscle pressure signal based on the target active segment and a second average value feature based on the inactive segment.

[0054] The second extraction unit is used to extract the root mean square feature, average absolute value feature, slope sign change rate feature, zero crossing rate feature, median frequency feature and average power frequency feature of the muscle physiological electrical signal.

[0055] Optionally, in one embodiment of the present invention, the prediction module 300 includes: a third extraction unit and a processing unit.

[0056] The third extraction unit is used to extract the signal characteristics of each muscle of at least one person to be tested under the target action type.

[0057] The processing unit is used to perform signal normalization processing on the signal features of at least one person to be tested based on the training label weight range to obtain processed features.

[0058] Optionally, in one embodiment of the present invention, the muscle strength prediction device 10 based on FMG and sEMG signals further includes: a determination module, an acquisition module and a generation module.

[0059] Among them, the determination module is used to determine the feature data set using the processed features before inputting the processed features into the target machine learning model, and divide the feature data set into a training set and a test set.

[0060] The acquisition module is used to train a pre-built machine learning model using a training set before inputting the processed features into a target machine learning model to obtain a trained machine learning model.

[0061] The generation module is used to input the test set into the trained machine learning model before inputting the processed features into the target machine learning model to output the model prediction results, and generate the target machine learning model when the model prediction results meet the preset test conditions.

[0062] It should be noted that the aforementioned explanation of the embodiment of the muscle strength prediction method based on FMG and sEMG signals is also applicable to the muscle strength prediction device 10 based on FMG and sEMG signals of this embodiment, and will not be repeated here.

[0063] According to the muscle strength prediction device based on FMG and sEMG signals proposed in the embodiment of the present invention, the muscle pressure signal generated by the muscle activity of the person to be tested can be collected based on the FMG technology, and the muscle physiological electrical signal generated by the muscle activity of the person to be tested can be collected based on the sEMG technology. Then, the target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, and standardized according to the target muscle strength level to obtain the processed features, and the processed features are input into the target machine learning model to output the muscle strength prediction result of the person to be tested, effectively reducing the cost requirements and improving the robustness of muscle strength prediction. Thus, the problem in the related art that the sEMG signal is easily interfered by noise and needs to use a high-density array for signal analysis, which increases the number of sensors, increases the difficulty of signal processing, and reduces the robustness of muscle strength prediction is solved.

[0064] Fig. 9 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0065] When the processor 902 executes the program, the muscle strength prediction method based on FMG and sEMG signals provided in the above embodiment is implemented.

[0066] Furthermore, the electronic device further comprises: The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0067] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0068] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0069] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0070] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0071] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0072] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned muscle strength prediction method based on FMG and sEMG signals is implemented.

[0073] This embodiment also provides a computer program product, including a computer program. When the computer program is executed, it is used to implement the above-mentioned muscle strength prediction method based on FMG and sEMG signals.

[0074] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0075] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0076] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0078] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0079] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0080] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0081] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A muscle strength prediction method based on FMG and sEMG signals, characterized in that: The following steps are involved: Based on the muscle pressure graph FMG technology, the muscle pressure signal generated by the muscle activity of at least one person to be tested is collected; Based on the surface electromyography (sEMG) technology, collecting muscle physiological electrical signals generated by muscle activity of at least one person to be tested; The target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal are extracted, and the target time domain features and the target frequency domain features are standardized according to the target muscle strength level to obtain processed features, and the processed features are input into the target machine learning model to output the muscle strength prediction result of the at least one person to be tested.

2. The muscle strength prediction method based on FMG and sEMG signals according to claim 1, characterized in that: The step of extracting target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal includes: Extracting a first average value feature of the muscle pressure signal based on the target active segment and a second average value feature based on the inactive segment; The root mean square feature, the average absolute value feature, the slope sign change rate feature, the zero crossing rate feature, the median frequency feature and the average power frequency feature of the muscle physiological electrical signal are extracted.

3. The muscle strength prediction method based on FMG and sEMG signals according to claim 1, characterized in that: The target time domain features and the target frequency domain features are normalized according to the target muscle strength level to obtain the processed features, including: Under the target action type, extracting the signal feature of each muscle of the at least one person to be tested; Based on the training label weight range, signal normalization processing is performed on the signal features of the at least one person to be tested to obtain the processed features.

4. The muscle strength prediction method based on FMG and sEMG signals according to claim 1, characterized in that: Before inputting the processed features into the target machine learning model, the method further includes: Determine a feature data set using the processed features, and divide the feature data set into a training set and a test set; Using the training set to train a pre-built machine learning model to obtain a trained machine learning model; The test set is input into the trained machine learning model to output the model prediction result, and when the model prediction result meets the preset test conditions, the target machine learning model is generated.

5. A muscle strength prediction device based on FMG and sEMG signals, characterized in that: include: The first acquisition module is used to acquire a muscle pressure signal generated by muscle activity of at least one person to be tested based on the muscle pressure graph FMG technology; A second acquisition module is used to acquire muscle physiological electrical signals generated by muscle activity of the at least one person to be tested based on surface electromyography (sEMG) technology; A prediction module is used to extract target time domain features and target frequency domain features of the muscle pressure signal and the muscle physiological electrical signal, and standardize the target time domain features and the target frequency domain features according to the target muscle strength level to obtain processed features, and input the processed features into a target machine learning model to output the muscle strength prediction result of at least one person to be tested.

6. The muscle strength prediction device based on FMG and sEMG signals according to claim 5, characterized in that: The prediction module comprises: A first extraction unit is used to extract a first average value feature of the muscle pressure signal based on a target active segment and a second average value feature based on an inactive segment; The second extraction unit is used to extract the root mean square feature, the average absolute value feature, the slope sign change rate feature, the zero crossing rate feature, the median frequency feature and the average power frequency feature of the muscle physiological electrical signal.

7. The muscle strength prediction device based on FMG and sEMG signals according to claim 5, characterized in that: The prediction module comprises: A third extraction unit is used to extract the signal characteristics of each muscle of the at least one person to be tested under the target action type; The processing unit is used to perform signal normalization processing on the signal features of the at least one person to be tested based on the training label weight range to obtain the processed features.

8. The muscle strength prediction device based on FMG and sEMG signals according to claim 5, characterized in that: Also includes: A determination module, configured to determine a feature data set using the processed features before inputting the processed features into a target machine learning model, and divide the feature data set into a training set and a test set; An acquisition module, used for training a pre-built machine learning model using the training set before inputting the processed features into a target machine learning model to obtain a trained machine learning model; A generation module is used to input the test set into the trained machine learning model before inputting the processed features into the target machine learning model to output the model prediction results, and generate the target machine learning model when the model prediction results meet the preset test conditions.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the muscle strength prediction method based on FMG and sEMG signals as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the muscle strength prediction method based on FMG and sEMG signals as described in any one of claims 1 to 4.

11. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the muscle strength prediction method based on FMG and sEMG signals as described in any one of claims 1 to 4.

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