Muscle multi-modal monitoring and prediction system and method, equipment, medium and product

Abstract

The invention provides a muscle multi-mode monitoring and predicting system and method, equipment, a medium and a product. An EMG electrode in the system is used for collecting muscle electric signals; the MMG vibrating reed is used for collecting muscle vibration signals; the near-infrared spectrometer is used for collecting the oxygen content of the muscle; the main control module is used for monitoring the muscle electric signal and the muscle vibration signal, collecting the change of the muscle oxygen content to determine the rehabilitation treatment effect, and predicting the subsequent rehabilitation effect of the patient according to the monitoring result and the big data so as to formulate a targeted rehabilitation strategy according to the prediction result. Detection of three different physiological signals is integrated, changes in the rehabilitation process can be monitored from different angles, and all-around theoretical support is provided for determining a rehabilitation scheme; the monitored signals of different rehabilitation stages and the duration time of the rehabilitation stages can be classified, a database is obtained through methods such as big data, and the rehabilitation process and the rehabilitation effect of the stroke patient are predicted through methods such as machine learning.

Description

Technical Field

[0001] The present invention relates to the field of rehabilitation monitoring and prediction technology, and in particular to a muscle multimodal monitoring and prediction system and method, equipment, medium and product. Background Art

[0002] Electromyographic signals (EMG) have always played an important role in detecting the effect of limb rehabilitation in stroke patients. At present, there are multi-channel EMG signal detection methods for EMG signal reception. This method can eliminate the randomness brought by single-channel detection, and can compare the EMG signals of different muscle parts under the same conditions to obtain the best area for detecting EMG signals. However, multi-channel EMG detection devices are generally large in size and have a large number of sensors. They are inconvenient for patients to wear and will be disturbed when moving. In addition to electrical signals, physiological signals also include chemical signals, such as muscle oxygen content, and the changes in these signals during rehabilitation cannot be ignored.

[0003] Therefore, it is necessary to optimize the monitoring and prediction of human muscle physiological signals to improve the above problems. Summary of the invention

[0004] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a muscle multimodal monitoring and prediction system, including EMG electrodes, MMG vibration sheets, near-infrared spectrometers, and main control modules; wherein, The EMG electrodes are used to collect muscle electrical signals; The MMG vibration piece is used to collect muscle vibration signals; The near infrared spectrometer is used to collect muscle oxygen content; The main control module is used to monitor the muscle electrical signals, the muscle vibration signals, and the changes in the collected muscle oxygen content to determine the rehabilitation treatment effect, and predict the patient's subsequent rehabilitation effect based on the monitoring results and big data, so as to formulate targeted rehabilitation strategies based on the predicted results.

[0005] Furthermore, the EMG electrode includes a differential electrode and a reference electrode, the differential electrode receives a voltage value relative to the reference electrode respectively, and the muscle electrical signal is obtained through the voltage difference between the differential electrodes.

[0006] Furthermore, the MMG vibration sheet uses a high-sensitivity microphone, which is used to detect the lateral integrated vibration caused by asynchronous muscle fiber activation, the change in muscle fiber size dominated by the motor unit, and the tiny vibration generated by the continuous muscle contraction.

[0007] Furthermore, the near-infrared spectrometer adopts a pair of near-infrared light sources and detectors, wherein the near-infrared light source is used to emit near-infrared light of multiple different wavelength bands, and the detector is used to detect the light signal returned to the skin surface.

[0008] Furthermore, the distance between the near-infrared light source and the detector is configured to ensure that the detection depth is greater than the thickness of the surface muscle tissue while making the received light intensity as large as possible.

[0009] The second object of the present invention is to provide a muscle multimodal monitoring and prediction method, which is implemented based on the above system and includes the following steps: Acquire multimodal muscle physiological signals during the patient's rehabilitation process; wherein the multimodal muscle physiological signals include muscle electrical signals, muscle vibration signals, and muscle oxygen content; Monitoring changes in the multimodal physiological signals of the muscles to determine the effect of rehabilitation treatment; The patient's subsequent rehabilitation effect can be predicted based on the monitoring results and big data, so that targeted rehabilitation strategies can be formulated based on the predicted results.

[0010] Furthermore, the step of monitoring the changes of the multimodal physiological signals of the muscles to determine the effect of the rehabilitation treatment includes: Obtain multimodal physiological signals of the patient's healthy muscles as evaluation reference signals; comparing the patient's muscle multimodal physiological signals during the rehabilitation process with the evaluation reference signals; The effectiveness of rehabilitation treatment is determined based on the comparison results.

[0011] Furthermore, the method further comprises the steps of: Form a database of rehabilitation time and rehabilitation effect through multimodal muscle physiological signals of multiple patients; Use machine learning algorithms to build predictive models; The prediction model is trained based on the rehabilitation time and rehabilitation effect database.

[0012] Furthermore, the step of forming a rehabilitation time and rehabilitation effect database through muscle multimodal physiological signals of multiple patients includes: The multimodal muscle physiological signals of patients at different rehabilitation stages and the duration of the rehabilitation stage are classified, and then a database is obtained through big data analysis methods.

[0013] Furthermore, the steps of predicting the subsequent rehabilitation effect of the patient based on the monitoring results and big data, and formulating targeted rehabilitation strategies based on the predicted results include: The monitoring results are used as input of the prediction model to predict the patient's subsequent rehabilitation effect.

[0014] A third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0015] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0016] A fifth object of the present invention is to provide a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The muscle multimodal monitoring and prediction system and method provided by the present invention integrate the detection of three different physiological signals: electrical signal EMG, muscle vibration signal MMG and muscle oxygen content. It can monitor the changes in the rehabilitation process from different angles and provide comprehensive theoretical support for determining the rehabilitation plan. The method can also classify the monitored signals of different rehabilitation stages and the duration of the rehabilitation stage, and then obtain a database through big data and other methods, and predict the rehabilitation process and rehabilitation effect of stroke patients through machine learning and other methods.

[0018] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a schematic diagram of muscle multimodal physiological signal acquisition in Example 1; Figure 2 This is a schematic diagram of muscle electrical signal collection in Example 1; Figure 3 This is a schematic diagram of muscle vibration signal collection in Example 1; Figure 4 This is a schematic diagram of muscle oxygen content collection in Example 1; Figure 5 This is a flow chart of the muscle multimodal monitoring and prediction method of Example 2; Figure 6 The flowchart of monitoring changes of multimodal muscle physiological signals of Example 2; Figure 7 This is a flow chart of the prediction model training of Example 2; Figure 8 This is a schematic diagram of a computer device in Example 3; Fig. 9 This is a schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0021] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0022] The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0024] Example 1 A muscle multimodal monitoring and prediction system, mainly for patients with congenital or acquired muscle paralysis caused by diseases such as stroke. Figure 1 As shown, the system includes EMG electrodes, MMG vibrating sheets, near-infrared spectrometers, and a main control module; wherein, The EMG electrodes are used to collect muscle electrical signals; The MMG vibration piece is used to collect muscle vibration signals; The near infrared spectrometer is used to collect muscle oxygen content; The main control module is used to monitor the muscle electrical signals, the muscle vibration signals, and the changes in the collected muscle oxygen content to determine the rehabilitation treatment effect, and predict the patient's subsequent rehabilitation effect based on the monitoring results and big data, so as to formulate targeted rehabilitation strategies based on the predicted results.

[0025] Specifically, the changes in the patient's muscle multimodal physiological signals can be monitored during the rehabilitation treatment process to determine the effectiveness of the rehabilitation treatment. The patient's subsequent rehabilitation effect can also be predicted based on the monitoring results and big data, allowing rehabilitation personnel to develop targeted rehabilitation strategies based on the predicted results.

[0026] Optionally, the patient's healthy muscle multimodal physiological signals are first collected as a reference for evaluation, and then the affected muscle multimodal physiological signals are collected during the rehabilitation process. By comparing the signal differences between the two, the patient's rehabilitation status can be more intuitively observed. After collecting muscle multimodal physiological signals from multiple patients, a database of rehabilitation time and rehabilitation effect can be obtained. The database can be applied to other patients based on machine learning and other methods to obtain the patient's expected rehabilitation period, etc.

[0027] Take stroke as an example. The clinical manifestations of stroke patients include hemiplegia of limbs, muscle weakness and other symptoms. Through external assistance, the affected part can gradually recover its original function. As for the legs, existing technologies include pneumatic exoskeletons, bicycle-type exercise devices, etc. This recovery process generally lasts for a long time, so an indicator is needed to determine the effect of rehabilitation and record the duration of different rehabilitation processes.

[0028] When the human body performs muscle activities autonomously, the muscles will generate a series of physiological signals, among which the most obvious and easiest to detect is the muscle electrical signal EMG. When the brain controls muscle movement, bioelectrical signals will flow from the brain to the controlled part. Therefore, when the patient can autonomously control the affected part, the affected part that would not detect electrical signals due to paralysis will be detected by EMG, and the better the rehabilitation effect, the stronger the EMG, and eventually it will reach the same level as the healthy part. Similarly, the muscle vibration signal MMG is the same. When the affected part performs autonomous movement, it will also produce slight vibration changes in the muscle, and this change will gradually increase with the progress of rehabilitation. When the muscle contracts, the oxygenated hemoglobin HbO2 in the muscle will provide oxygen for the oxidation of fat and sugar, thereby providing energy for muscle contraction. At this time, the oxygenated hemoglobin will be converted into deoxyhemoglobin HHb, so the information of muscle movement can be obtained by detecting the changes in the content of these two proteins in the muscle.

[0029] This embodiment realizes the acquisition, modulation and transmission of multiple muscle physiological signals of the human body, and can intuitively display the waveform of each signal when the muscle is active in the form of images. It also has the function of saving data to compare the treatment effects before and after and make subsequent predictions.

[0030] The detection of muscle electrical signals is directly collected by EMG electrodes placed on the skin, such as Figure 2As shown, the EMG electrode includes a differential electrode and a reference electrode. The differential electrode receives a voltage value relative to the reference electrode respectively, and the muscle electrical signal is obtained through the voltage difference between the differential electrodes.

[0031] Specifically, the two differential electrodes each receive a voltage value relative to the reference electrode, and the voltage difference between the two differential electrodes is the muscle electrical signal EMG. When the muscle cannot contract, the voltage difference between the two differential electrodes is zero, so the detected EMG signal is zero. As the rehabilitation process of the affected part progresses, the patient can gradually control the contraction of the affected muscle autonomously. At this time, current will be generated from the cerebral cortex to the affected part, and a voltage difference will be generated between the differential electrodes. The voltage difference will also gradually increase, and finally reach the same level as the healthy part when the rehabilitation is completed.

[0032] The principle of muscle vibration signal MMG is as follows Figure 3 As shown, when the patient can autonomously control the contraction of the affected muscle, the muscle fibers will produce low-frequency vibrations, which will cause a displacement of about 500 mm on the skin surface. The vibration signal can be divided into lateral integrated vibrations caused by asynchronous activation of muscle fibers, changes in muscle fiber size dominated by motor units, and tiny vibrations caused by continuous muscle contraction. This embodiment uses a high-sensitivity microphone as a vibrating piece for collecting MMG signals to ensure the accuracy of the received MMG. This microphone can detect tiny muscle vibrations, thereby obtaining muscle vibration signals, i.e., muscle sound type variables.

[0033] The principle of using near-infrared NIRS to detect muscle oxygen content is as follows Figure 4 As shown, the near-infrared spectrometer uses a pair of near-infrared light source LEDs ( Figure 4 The transmitter in the Figure 4 The receiving end in the NIRS. When the wavelength of light is between 650nm and 1000nm, human tissue is transparent to NIRS. At this time, the main substances that absorb light in the muscle are water, melanin, bilirubin, HbO2 and HHb, of which only HbO2 and HHb are substances sensitive to muscle contraction. According to the characteristics of the hemoglobin absorption spectrum, the light source can emit three different bands of near-infrared light, namely around 800nm, below 800nm ​​and above 800nm. Studies have shown that photons emitted by the transmitting end will scatter in a banana shape and return to the skin surface after multiple scatterings. The theoretical maximum transmission depth of near-infrared light is about half of the distance between the light source and the detector, but as the distance increases, the received light intensity will decrease in geometric series, so the set distance needs to ensure that the detection depth is greater than the thickness of the surface muscle tissue while making the received light intensity as large as possible. The distance varies from person to person, so the specific data is not limited in this embodiment.

[0034] Taking the arm as an example, the placement of EMG electrodes, MMG vibrating sheets, near-infrared light sources and detectors is as follows: Figure 1 As shown, EMG signals are collected through EMG electrodes attached to the skin surface, MMG signals are collected through MMG vibrating sheets attached to the skin surface, and muscle oxygen content is collected through infrared light NIRS measurement.

[0035] A muscle multimodal monitoring and prediction system provided in this embodiment integrates the detection of three different physiological signals: electrical signal EMG, muscle vibration signal MMG and muscle oxygen content. It can monitor the changes in the rehabilitation process from different angles and provide comprehensive theoretical support for determining the rehabilitation plan; the system can also classify the monitored signals of different rehabilitation stages and the duration of the rehabilitation stage, and then obtain a database through big data and other methods, and predict the rehabilitation process and rehabilitation effect of stroke patients through machine learning and other methods.

[0036] Example 2 A muscle multimodal monitoring and prediction method is implemented based on the above system. For a detailed description of the system, please refer to the corresponding description in the above system embodiment, which will not be repeated here. Figure 5 As shown, the method comprises the following steps: S1. Acquire multimodal muscle physiological signals of the patient during the rehabilitation process; wherein the multimodal muscle physiological signals include muscle electrical signals, muscle vibration signals and muscle oxygen content; Among them, the specific collection scheme of muscle electrical signals, muscle vibration signals and muscle oxygen content can refer to the corresponding description in the above system embodiment, which will not be repeated here.

[0037] S2. monitoring changes in the multimodal physiological signals of the muscles to determine the effect of rehabilitation treatment; In some embodiments, Figure 6 As shown, the step of monitoring the changes of the multimodal physiological signals of the muscles to determine the effect of rehabilitation treatment includes: S21, obtaining a multimodal physiological signal of a healthy muscle of the patient as an evaluation reference signal; S22, comparing the patient's muscle multimodal physiological signal during the rehabilitation process with the evaluation reference signal; S23. Determine the effect of the rehabilitation treatment based on the comparison results.

[0038] In this embodiment, the patient's recovery condition can be observed more intuitively by comparing the signal difference between the two.

[0039] In some embodiments, Figure 7 As shown, the steps include: S300, forming a rehabilitation time and rehabilitation effect database through muscle multimodal physiological signals of multiple patients; In some optional embodiments, the step of forming a rehabilitation time and rehabilitation effect database using muscle multimodal physiological signals of multiple patients includes: The multimodal muscle physiological signals of patients at different rehabilitation stages and the duration of the rehabilitation stage are classified, and then a database is obtained through big data analysis methods.

[0040] S310, use machine learning and other algorithms to build prediction models; S320: training the prediction model based on the rehabilitation time and rehabilitation effect database.

[0041] S3. Predict the patient's subsequent rehabilitation effect based on monitoring results and big data, and formulate targeted rehabilitation strategies based on the predicted results.

[0042] In combination with the above embodiment, the steps of predicting the subsequent rehabilitation effect of the patient based on the monitoring results and big data, and formulating a targeted rehabilitation strategy based on the predicted results include: The monitoring results are used as input of the prediction model to predict the patient's subsequent rehabilitation effect.

[0043] This embodiment applies the database to other patients based on machine learning and other methods to obtain the patient's expected recovery period and the like.

[0044] A muscle multimodal monitoring and prediction method provided in this embodiment integrates the detection of three different physiological signals: electrical signal EMG, muscle vibration signal MMG and muscle oxygen content. It can monitor the changes in the rehabilitation process from different angles and provide comprehensive theoretical support for determining the rehabilitation plan. The method can also classify the monitored signals of different rehabilitation stages and the duration of the rehabilitation stage, and then obtain a database through big data and other methods, and predict the rehabilitation process and rehabilitation effect of stroke patients through machine learning and other methods.

[0045] Example 3 A computer device 400, such as Figure 8 As shown, it includes a memory 410, a processor 420, and a computer program 430 stored in the memory and executable on the processor, and the processor implements the steps of a muscle multimodal monitoring and prediction method when executing the computer program. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0046] Example 4 A computer readable storage medium such as Fig. 9As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a muscle multimodal monitoring and prediction method are implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, and no further description is given here.

[0047] Example 5 A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of a muscle multimodal monitoring and prediction method are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0048] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0049] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0050] The apparatus, computer device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, computer device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device and non-volatile computer storage medium will not be repeated here.

[0051] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0052] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0053] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0054] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0055] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0057] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0058] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0059] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0060] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A muscle multimodal monitoring and prediction system, characterized in that: It includes EMG electrodes, MMG vibrating sheets, near-infrared spectrometers, and main control modules; among them, The EMG electrodes are used to collect muscle electrical signals; The MMG vibration piece is used to collect muscle vibration signals; The near infrared spectrometer is used to collect muscle oxygen content; The main control module is used to monitor the muscle electrical signals, the muscle vibration signals, and the changes in the collected muscle oxygen content to determine the rehabilitation treatment effect, and predict the patient's subsequent rehabilitation effect based on the monitoring results and big data, so as to formulate targeted rehabilitation strategies based on the predicted results.

2. A muscle multimodal monitoring and prediction system as claimed in claim 1, characterized in that: The EMG electrodes include differential electrodes and reference electrodes. The differential electrodes receive voltage values ​​relative to the reference electrodes respectively, and the muscle electrical signals are obtained through the voltage difference between the differential electrodes.

3. A muscle multimodal monitoring and prediction system as claimed in claim 1, characterized in that: The MMG vibration sheet uses a highly sensitive microphone, which is used to detect the lateral integrated vibration caused by asynchronous muscle fiber activation, the change in muscle fiber size dominated by the motor unit, and the tiny vibrations generated by the continuous muscle contraction.

4. A muscle multimodal monitoring and prediction system as claimed in claim 1, characterized in that: The near-infrared spectrometer uses a pair of near-infrared light sources and detectors. The near-infrared light source is used to emit near-infrared light of multiple different wavebands, and the detector is used to detect the optical signal returned to the skin surface.

5. A muscle multimodal monitoring and prediction system as claimed in claim 4, characterized in that: The distance between the near-infrared light source and the detector is configured to ensure that the detection depth is greater than the thickness of the surface muscle tissue while making the received light intensity as large as possible.

6. A muscle multimodal monitoring and prediction method, implemented based on the system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Acquire multimodal muscle physiological signals during the patient's rehabilitation process; wherein the multimodal muscle physiological signals include muscle electrical signals, muscle vibration signals, and muscle oxygen content; Monitoring changes in the multimodal physiological signals of the muscles to determine the effect of rehabilitation treatment; The patient's subsequent rehabilitation effect can be predicted based on the monitoring results and big data, so that targeted rehabilitation strategies can be formulated based on the predicted results.

7. A muscle multimodal monitoring and prediction method as claimed in claim 6, characterized in that: The step of monitoring the changes of the multimodal physiological signals of the muscles to determine the effect of the rehabilitation treatment comprises: Obtain multimodal physiological signals of the patient's healthy muscles as evaluation reference signals; comparing the patient's muscle multimodal physiological signals during the rehabilitation process with the evaluation reference signals; The effectiveness of rehabilitation treatment is determined based on the comparison results.

8. A muscle multimodal monitoring and prediction method as claimed in claim 6, characterized in that: Also includes the steps: Form a database of rehabilitation time and rehabilitation effect through multimodal muscle physiological signals of multiple patients; Use machine learning algorithms to build predictive models; The prediction model is trained based on the rehabilitation time and rehabilitation effect database.

9. A muscle multimodal monitoring and prediction method as claimed in claim 8, characterized in that: The step of forming a database of rehabilitation time and rehabilitation effect through muscle multimodal physiological signals of multiple patients comprises: The multimodal muscle physiological signals of patients at different rehabilitation stages and the duration of the rehabilitation stage are classified, and then a database is obtained through big data analysis methods.

10. A muscle multimodal monitoring and prediction method as claimed in claim 8, characterized in that: The steps of predicting the patient's subsequent rehabilitation effect based on the monitoring results and big data, and formulating targeted rehabilitation strategies based on the predicted results include: The monitoring results are used as input of the prediction model to predict the patient's subsequent rehabilitation effect.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 6 to 10 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 6 to 10 are implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 6 to 10 are implemented.

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