阅读说明：本技术 肌肉训练仪的参数优化方法、系统、设备以及存储介质 (Parameter optimization method, system, equipment and storage medium of muscle training instrument ) 是由 章鸿 谢采风 杜建峰 林建勋 伍浩 于 2019-11-18 设计创作，主要内容包括：本申请涉及肌肉训练技术领域,具体公开了一种肌肉训练仪的参数优化方法、系统、设备以及存储介质,该方法包括：获取第一训练方案；在进行所述第一训练方案时,采集待训练肌肉的痉挛疲劳信息；根据痉挛疲劳信息调整第一训练方案的训练参数以生成第二训练方案。通过上述方式,本申请能够准确监测训练者是否肌肉痉挛和/或肌肉疲劳,进而根据痉挛疲劳信息调整第一训练方案的训练参数以生成第二训练方案,以及时对训练者的痉挛和/或疲劳症状进行处理。(The application relates to the technical field of muscle training, and particularly discloses a parameter optimization method, a parameter optimization system, a parameter optimization device and a parameter optimization storage medium of a muscle training instrument, wherein the method comprises the following steps: acquiring a first training scheme; acquiring spasm fatigue information of muscles to be trained when the first training scheme is carried out; the training parameters of the first training regimen are adjusted according to the seizure fatigue information to generate a second training regimen. By means of the mode, whether the trainer has muscle spasm and/or muscle fatigue can be accurately monitored, then the training parameters of the first training scheme are adjusted according to the spasm fatigue information to generate the second training scheme, and spasm and/or fatigue symptoms of the trainer are timely processed.)

1. A parameter optimization method of a muscle training instrument is characterized by comprising the following steps:

acquiring a first training scheme;

acquiring spasm fatigue information of muscles to be trained when the first training scheme is carried out;

adjusting training parameters of the first training scheme according to the seizure fatigue information to generate a second training scheme.

2. The method according to claim 1, wherein the first training regimen comprises a stress training regimen and/or a myoelectric training regimen, and the step of adjusting the training parameters of the first training regimen to generate the second training regimen in accordance with the seizure fatigue information comprises:

analyzing the spasm fatigue information to judge the muscle spasm type or the muscle fatigue degree of the muscle to be trained;

adjusting a stress parameter and/or an electrical stimulation current signal parameter of the stress training protocol to generate the second training protocol according to the muscle spasm type or the muscle fatigue level.

3. The method of claim 1, wherein the step of collecting spastic fatigue information of the muscle to be trained while performing the first training regimen comprises:

acquiring myoelectric signals and myotension images of the muscle to be trained when the user performs the first training scheme;

analyzing the myoelectric signal and the image of the myotension to obtain the spasm fatigue information, wherein the spasm fatigue information comprises at least one of a myoelectric value, a myotension value, a continuous active time of the myoelectric signal, an average power frequency of the myoelectric signal and a median frequency of the myoelectric signal.

4. The method of claim 3, wherein the step of analyzing the image of the myoelectric signal and the muscular tension to obtain the seizure fatigue information comprises:

filtering the electromyographic signals;

drawing a frequency domain-based frequency spectrum graph and a time domain-based potential graph of the filtered electromyographic signals;

reading the mean value of the amplitude values in the potential map to obtain the myoelectric value; or

Reading the average power frequency or the median frequency in the spectrogram.

5. The method of claim 3, wherein the step of analyzing the spasm fatigue information to determine the muscle spasm type of the muscle to be trained when the muscle information is a myoelectric value, a myotension value, and a continuous active time of a myoelectric signal comprises:

comparing the myoelectric value with a plurality of preset myoelectric value ranges, or the myoelectric signal continuous active time with a plurality of preset myoelectric signal continuous active time ranges;

determining a preset electromyographic value range where the electromyographic value is located, a preset myotonic value range where the myotonic value is located, and a preset electromyographic signal continuous active time range where the electromyographic signal continuous active time is located;

and determining the muscle spasm type corresponding to the myoelectricity value, the myotension value or the continuous active time of the myoelectricity signal based on the corresponding relationship between the muscle spasm type and a preset myoelectricity value range, a preset myotension value range or a preset continuous active time range of the myoelectricity signal.

6. The method according to claim 3, wherein when the muscle information is the mean power frequency or the median frequency of the electromyographic signals, the step of analyzing the spastic fatigue information to determine the muscle fatigue degree of the muscle to be trained comprises:

acquiring a curve of the average power frequency or the median frequency along with the change of time;

starting timing when the average power frequency or the median frequency decreases along with the increase of time;

comparing the duration of the decrease of the average power frequency or the median frequency with a plurality of preset time ranges;

determining a preset time range in which the duration of the average power frequency or the median frequency is reduced;

and determining the muscle fatigue degree corresponding to the duration of the reduction of the average power frequency or the median frequency based on the corresponding relation between the muscle fatigue degree and a preset time range.

7. A muscle training system, the system comprising: the processor is connected with the electrode device;

wherein the processor is configured to obtain a first training scheme;

when the first training scheme is carried out, the electrode device is used for acquiring spasm fatigue information of muscles to be trained;

the processor is configured to adjust the training parameters of the first training scheme according to the seizure fatigue information to generate a second training scheme.

8. The system of claim 7, wherein the electrode device comprises a carrier and an electrode pad disposed on an outer wall of the carrier, the carrier being an elastic balloon;

the elastic air bag is used for executing the pressure training scheme, and the electrode plate is used for executing the myoelectricity training scheme;

the processor is used for analyzing the spasm fatigue information to judge the muscle spasm type or the muscle fatigue degree of the muscle to be trained;

the processor is used for adjusting the pressure parameter of the pressure training scheme and/or the electric stimulation current signal parameter of the myoelectricity training scheme according to the muscle spasm type or the muscle fatigue degree to generate the second training scheme.

9. The system of claim 7,

the electrode device is used for acquiring myoelectric signals and muscle tension images of the muscle to be trained when the user performs the first training scheme;

the processor is used for receiving the electromyographic signal and analyzing the electromyographic signal and the image of the myotension to obtain the spasm fatigue information, wherein the spasm fatigue information comprises at least one of a myoelectric value, a myotension value, a continuous active time of the electromyographic signal, an average power frequency of the electromyographic signal and a median frequency of the electromyographic signal.

10. The system of claim 9,

the processor is used for filtering the electromyographic signals and drawing a frequency domain-based frequency spectrum graph and a time domain-based potential graph of the filtered electromyographic signals;

the processor is also used for reading the mean value of the amplitude values in the potential diagram to obtain the myoelectric value, or reading the average power frequency or median frequency in the spectrogram.

11. The system of claim 9,

the processor is used for comparing the myoelectric value with a plurality of preset myoelectric value ranges, or the myoelectric signal continuous active time with a plurality of preset myoelectric signal continuous active time ranges; determining a preset electromyographic value range where the electromyographic value is located, a preset myotonic value range where the myotonic value is located, and a preset electromyographic signal continuous active time range where the electromyographic signal continuous active time is located; and determining the muscle spasm type corresponding to the myoelectricity value, the myotension value or the continuous active time of the myoelectricity signal based on the corresponding relationship between the muscle spasm type and a preset myoelectricity value range, a preset myotension value range or a preset continuous active time range of the myoelectricity signal.

12. The system of claim 9,

the processor is used for acquiring a curve of the average power frequency or the median frequency along with the change of time; starting timing when the average power frequency or the median frequency decreases along with the increase of time; comparing the duration of the decrease of the average power frequency or the median frequency with a plurality of preset time ranges; determining a preset time range in which the duration of the average power frequency or the median frequency is reduced; and determining the muscle fatigue degree corresponding to the duration of the reduction of the average power frequency or the median frequency based on the corresponding relation between the muscle fatigue degree and a preset time range.

13. A muscle training apparatus, characterized in that the apparatus comprises: the device comprises an acquisition unit, an acquisition unit and a processing unit, wherein the processing unit is respectively connected with the acquisition unit and the acquisition unit; the processing unit, the acquisition unit and the acquisition unit cooperate with each other to implement the steps of the parameter optimization method of the muscle training apparatus according to any one of claims 1 to 6.

14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for optimizing parameters of a muscle training apparatus according to any one of claims 1 to 6.

Technical Field

The application relates to the technical field of muscle training, in particular to a parameter optimization method, a parameter optimization system, a parameter optimization device and a parameter optimization storage medium of a muscle training instrument.

Background

Electromyographic biofeedback training and neuromuscular electrical stimulation are conventional means of muscle training. In a long-term research and development process, the inventor of the application finds that the prior art cannot monitor the type of muscle spasm and/or muscle fatigue of a trainer in real time, and adopts a manual mode to massage muscles, so that the automation degree is low, and the spasm symptom of the trainer cannot be timely treated.

Disclosure of Invention

Based on this, there is a need for a method, a system, a device and a storage medium for optimizing parameters of a muscle training instrument, which can accurately monitor whether a trainer has muscle spasm and/or muscle fatigue, adjust training parameters of a first training scheme according to spasm fatigue information to generate a second training scheme, and timely treat spasm and/or fatigue symptoms of the trainer.

In one aspect, the present application provides a method for optimizing parameters of a muscle training apparatus, the method comprising: acquiring a first training scheme; acquiring spasm fatigue information of muscles to be trained when the first training scheme is carried out; the training parameters of the first training regimen are adjusted according to the seizure fatigue information to generate a second training regimen.

In another aspect, the present application provides a muscle training system comprising: the electrode device is connected with the processor; the processor is used for acquiring a first training scheme; when the first training scheme is carried out, the electrode device is used for acquiring spasm fatigue information of muscles to be trained; the processor is configured to adjust the training parameters of the first training regimen based on the seizure fatigue information to generate a second training regimen.

In yet another aspect, the present application provides a muscle training apparatus comprising: the device comprises an acquisition unit, a collection unit and a processing unit, wherein the processing unit is respectively connected with the acquisition unit and the collection unit; the processing unit, the acquisition unit and the acquisition unit are mutually matched to realize the steps of the parameter optimization method of the muscle training instrument.

In yet another aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the aforementioned method for parameter optimization of a muscle training apparatus.

The beneficial effect of this application is: different from the situation of the prior art, when the first training scheme is carried out, the spasm fatigue information of the muscle to be trained is collected, so that the type of the muscle spasm and/or the muscle fatigue of the trainer can be accurately monitored, the training parameters of the first training scheme are adjusted according to the spasm fatigue information to generate a second training scheme, and the spasm and/or fatigue symptoms of the trainer can be timely processed.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:

FIG. 1 is a schematic flow chart diagram of an embodiment of a setup method for a muscle training program of the present application;

FIG. 2 is a schematic flow chart of step S30 in FIG. 1;

FIG. 3 is a schematic flow chart of step S20 in FIG. 1;

FIG. 4 is a schematic flow chart of step S22 in FIG. 3;

FIG. 5 is a schematic flow chart of step S31 in FIG. 2;

FIG. 6 is another schematic flow chart of step S31 in FIG. 2;

FIG. 7 is a schematic diagram of the structure of an embodiment of the muscle training system of the present application;

FIG. 8 is a schematic diagram of the structure of an embodiment of the muscle training apparatus of the present application;

FIG. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a setup method of a muscle training program of the present application, the method including the following steps:

s10: a first training scenario is obtained.

Specifically, the first training scheme is a current training scheme of the trainer, and the training scheme is composed of a plurality of training parameters, for example: at least one of training times, training duration, training actions, electrical stimulation current signals and air pressure of the air bag, wherein the training actions can be as follows: can be used in standing position, sitting position and lying position. The anus is contracted first, and then the urethra is contracted, so that the sense of lifting the levator ani muscle is generated. The thigh and abdominal muscles remain relaxed during the anus, urethra and contraction. Continuously contracting and extracting the anus for not less than 3 seconds, relaxing and resting for 2-6 seconds, and continuously doing for 15-30 minutes.

S20: when the first training scheme is carried out, spasm fatigue information of the muscle to be trained is collected.

Specifically, in this embodiment, only whether the muscle to be trained that is relevant to the trainer has spasm and/or fatigue when performing the first training scheme is considered, the spasm fatigue information of the muscle to be trained may be collected by the electrode device, the spasm fatigue information of the muscle may be myoelectric information or myotension information of the muscle, and the muscle spasm type or the muscle fatigue degree may be obtained by analyzing the spasm fatigue information of the muscle. For example, the electromyographic signals of the muscles to be trained in the training process are collected, and the muscle spasm type or the muscle fatigue degree can be known by analyzing the electromyographic signals.

Considering that the muscle to be trained may be provided in a plurality of parts, the number of the myoelectric channels of the electrode device may be set according to the number of the parts of the muscle to be trained, each of the myoelectric channels corresponding to a different part of the muscle to be trained. For example, the number of myoelectric channels includes at least 2 channels and at most 128 channels, wherein if there are 4 groups of myoelectric channels, 4 myoelectric signals can be acquired simultaneously.

Each group of myoelectricity channels comprises a plurality of electrode plates, such as 2 electrode plates, which are used for collecting myoelectricity signals, and the plurality of electrode plates forming one group of myoelectricity channels are arranged on the same muscle. For example, the electrode pads are respectively placed at the muscle abdomens of gluteus maximus, quadriceps femoris, biceps femoris and gastrocnemius muscle groups of the left and right legs, or at the triceps brachii, the deltoid anterior, the serratus anterior and the brachiocephalus of the upper limb, or at the levator ani, the anal sphincter, the obturator muscle, the urethral sphincter and the vaginal sphincter at the pelvic floor, so as to collect the myoelectric signals of the muscle portions.

S30: the training parameters of the first training regimen are adjusted according to the seizure fatigue information to generate a second training regimen.

Specifically, for different muscle fatigue degrees, the adjustment strategy of the training scheme is specifically as follows:

the first-stage fatigue degree represents that the fatigue degree is low, so that the training intensity can be improved, the training time can be prolonged, and/or the training strategy can be replaced;

the second-level fatigue degree represents the moderate fatigue degree, and the current training intensity and the training strategy can be kept;

a third level of fatigue, which indicates a higher level of fatigue, at which time the training intensity can be reduced, the training time can be reduced and/or the elastic air bags of the electrode devices can be controlled to inflate and deflate, so as to massage the fatigue muscles;

and a fourth fatigue degree which represents that the preset fatigue degree threshold value is reached or exceeded, and the output of the electrical stimulation current signal can be stopped or an alarm is given to remind the trainer to suspend the training.

The muscle spasm types comprise three types of muscle stiffness, muscle clonus and painful spasm, and aiming at different muscle spasm types, the adjustment strategy of the training scheme is as follows:

the training intensity can be reduced, the training time can be shortened and/or the elastic air bag of the electrode device can be controlled to be inflated and deflated when the muscle is strong or clonic, so that the fatigue muscle can be massaged;

the output of the electrical stimulation current signal can be stopped or an alarm can be given to remind the trainer to pause the training when the cramp is painful.

In other embodiments, the spasm fatigue information may be displayed by the processor to the background workstation, and the background workstation may make corresponding adjustments to the training parameters of the first training protocol based on the comparison of the collected spasm fatigue information and the actually output electrical stimulation current signal.

Different from the situation of the prior art, when the first training scheme is carried out, the spasm fatigue information of the muscle to be trained is collected, so that the type of the muscle spasm and/or the muscle fatigue of the trainer can be accurately monitored, the training parameters of the first training scheme are adjusted according to the spasm fatigue information to generate a second training scheme, and the spasm and/or fatigue symptoms of the trainer can be timely processed.

Referring to fig. 2, fig. 2 is a schematic flowchart of step S30 in fig. 1, wherein the first training scheme includes a stress training scheme and/or a myoelectricity training scheme. The step S30 includes:

s31: analyzing the spasm fatigue information to judge the muscle spasm type or muscle fatigue degree of the muscle to be trained.

Specifically, the endogenous fatigue degree and the exogenous fatigue degree of the muscle can be quantified according to spasm fatigue information (for example, bioelectrical activity such as the transmission speed of an electric signal), and the training parameters of the first training scheme can be adjusted based on the obtained comprehensive fatigue degree of the endogenous fatigue degree and the exogenous fatigue degree. Wherein, the exogenous fatigue of the muscle refers to the muscle fatigue caused by insufficient supply of substances, and the oxygen supply and the oxygen lack of the training muscle can be accurately mastered by monitoring the blood supply, the oxygen consumption and the like of the training muscle, so that the fatigue degree of the muscle is measured. The muscle endogenous fatigue refers to the fatigue of nerve and muscle tissues, and the fatigue degree of the muscle is measured by detecting bioelectricity activities such as the transmission speed and periodicity of electric signals of training muscles.

The muscle tension image under the muscle spasm state can be acquired through the muscle tension sensor, and the spasm characteristics can be judged according to the amplitude and the frequency of the muscle tension, so that the spasm type can be determined to be any one of myotonia, myoclonus and painful spasm.

S32: and adjusting the pressure parameter of the pressure training scheme and/or the electrical stimulation current signal parameter of the myoelectricity training scheme according to the muscle spasm type or the muscle fatigue degree to generate a second training scheme.

Specifically, the electrode device comprises an air bag, an inflation pipeline and a deflation pipeline, wherein the air bag is connected with an external air pump through the inflation pipeline. And when the pressure parameters of the pressure training scheme are adjusted, the inflation and deflation switch controls the air pump to inflate and deflate the air bag. Wherein, the air bag is made of elastic material, so that when the air bag is protruded towards one side of the muscle, the air bag can not cause damage to the muscle. The air pump is started to inflate the air bag, and the air bag is inflated and deflated regularly under the action of the inflation and deflation switch, so that the strength and the speed of the muscle massage process are realized, and the spasm condition and/or the muscle fatigue condition of the target muscle are/is relieved quickly.

Parameters of the electrical stimulation current signal include in particular the stimulation intensity, pulse width, pulse frequency and stimulation duration of the current. It can be understood that, for different spasticity conditions and/or muscle fatigue conditions, the parameters of the adjusted electrical stimulation current signals are different, and therefore, the parameters of the four electrical stimulation current signals can be set according to the actual requirements of the user, which is not limited herein.

Referring to fig. 3, fig. 3 is a schematic flowchart of step S20 in fig. 1. In one embodiment, step S20 includes:

s21: collecting myoelectric signals and muscle tension images of muscles to be trained when a user performs a first training scheme.

Specifically, myoelectric signals or muscle tension values of muscles are collected by corresponding electrode devices.

The electrode device comprises a carrier, and an electrode plate and a muscle tension sensor which are arranged on the outer wall of the carrier, wherein the carrier is an elastic air bag. Every two electrode plates form a corresponding myoelectric channel, every two electrode plates can be connected with the 1-path differential signal acquisition circuit through an analog switch circuit, then myoelectric signals of muscles are acquired through the differential signal acquisition circuit, and myotension images of the muscles in a normal state are acquired through the myotension sensor.

The muscle to be trained comprises two motion states, namely a force-exerting state and a relaxation state, and the electromyographic signals are obviously different in different motion states, so that the electromyographic signals have a specific rule when a user carries out a first training scheme. For example, if the exercise corresponding to the time T1 is "lying" and the exercise corresponding to the time T2 is "rising" during the exercise of the first training program as the sit-up, the muscle to be trained during the first training program is in a relaxed state at the time T1, and the muscle to be trained during the first training program is in a stressed state at the time T2. Therefore, theoretically, if the user performs the first training scenario, the electromyographic signals EMG1 and EMG2 corresponding to the time T1 and the time T2, that is, the electromyographic signals EMG1 and the electromyographic signals EMG2, are collected to have a specific rhythm.

S22: and analyzing the myoelectric signal and the image of the muscle tension to obtain spasm fatigue information.

The spasm fatigue information comprises at least one of a myoelectric value, a myotension value, a continuous active time of the myoelectric signal, an average power frequency of the myoelectric signal and a median frequency of the myoelectric signal.

And obtaining a muscle tension value by extracting the amplitude and frequency distribution condition of the muscle tension in the image.

Referring to fig. 4, fig. 4 is a schematic flowchart illustrating the process of step S22 in fig. 3, wherein, in an embodiment, step S22 includes:

s221: and filtering the electromyographic signals.

Specifically, each 1 circuit of differential signal acquisition circuit includes a low-pass filter, and in the signal acquisition process, the electromyographic signals acquired by the differential signal acquisition circuit can be filtered through the low-pass filter, so that high-frequency noise can be filtered, low-frequency signals can be filtered through the EMI filter, electromagnetic interference signals can be filtered, and the filtered electromyographic signals can be obtained.

S222: and drawing a frequency domain-based frequency spectrum graph and a time domain-based potential graph of the filtered electromyographic signals.

Specifically, when the electrode device collects the electromyographic signals during the movement of the muscle to be trained, the processor can extract the collected electromyographic signals from the electrode device, wherein the processor can be a computer or other equipment provided with MATLAB software.

The processor will firstly filter the electromyographic signals to remove the interference signals, and because the actual electromyographic signals are generally weak, the interference signals are difficult to filter completely by only adopting a common filter. Therefore, MATLAB software is used for processing the acquired electromyographic signals in the application.

After the electromyographic signals are filtered, a characteristic diagram of the electromyographic signals can be drawn. The electromyographic signals can be analyzed in a time domain or a frequency domain. If the electromyogram signal is suitable for analysis, the electromyogram may be plotted from the calculated root mean square value by calculating the root mean square of the electromyogram signal. If the frequency domain of the electromyographic signal is analyzed, a frequency spectrogram corresponding to the electromyographic signal can be drawn by performing fast Fourier transform.

S223: the mean of the amplitudes in the potential map was read to obtain the myoelectric values.

Specifically, after the processor plots the corresponding potential map and spectrogram according to different processing modes, characteristic information values can be acquired from the potential map and spectrogram to analyze the characteristic data. Further, when the electromyogram signal is analyzed through time domain processing, a characteristic information value may be extracted from a potential map corresponding to the electromyogram signal to analyze the electromyogram signal. The characteristic information value may be a variance equivalent value. When the electromyographic signal is analyzed by frequency domain processing, the feature data may be analyzed from a spectrogram corresponding to the electromyographic signal. The characteristic data can comprise the muscle strength level of the muscle to be trained, the local fatigue degree, the excitation conduction speed of a motor unit, the multi-muscle group coordination and other various muscle activities and the change rule of the central control characteristic.

The method comprises the steps of obtaining a spatial characteristic value of a potential diagram, and obtaining a myoelectric value in a muscle training process according to the spatial characteristic value. The spatial characteristic values include an amplitude mean value, a maximum amplitude, a barycentric coordinate, a maximum coordinate and the like.

Alternatively, the process proceeds to S224: the average power frequency or median frequency in the spectrogram is read.

Specifically, the average power frequency or the median frequency in the spectrogram is the average power frequency of the electromyographic signal or the median frequency of the electromyographic signal. And extracting characteristic information values from the frequency spectrogram to analyze the information of the electromyographic signals so as to quickly and accurately analyze the acquired electromyographic signals. The characteristic information includes average power frequency, median frequency, and the like.

Referring to fig. 5, fig. 5 is a flowchart illustrating the process of step S31 in fig. 2, wherein in an embodiment, when the muscle information is a myoelectric value, a myotension value, and a continuous active time of the myoelectric signal, step S31 includes:

s311: and comparing the myoelectric value with a plurality of preset myoelectric value ranges, or the continuous active time of the myoelectric signal with a plurality of preset myoelectric signal continuous active time ranges.

Specifically, when a muscle spasm occurs, the myoelectric value is greater than or equal to the myoelectric value in the tensioned state, and the continuous active time of the myoelectric signal is greater than or equal to the continuous active time of the myoelectric signal in the tensioned state, so that the largest myoelectric value among a plurality of myoelectric values in different muscle spasm types is taken as the preset myoelectric value corresponding to the muscle spasm type, and the largest myoelectric signal continuous active time among the plurality of myoelectric signal continuous active times in different muscle spasm types is taken as the preset myoelectric signal continuous active time corresponding to the muscle spasm type.

S312: and determining a preset myoelectric value range where the myoelectric value is located, and a preset myoelectric signal continuous active time range where the myoelectric signal continuous active time is located.

S313: and determining the muscle spasm type corresponding to the myoelectricity value, the myoelectricity value or the myoelectricity signal continuous active time based on the muscle spasm type and the corresponding relation of a preset myoelectricity value range, a preset myoelectricity value range or a preset myoelectricity signal continuous active time range.

Furthermore, the image of the muscle tension in the normal state of the muscle can be acquired by using the muscle tension sensor, and the amplitude and the frequency distribution of the muscle tension are classified through the processing integration of the host computer and the upper computer and are used as characteristic information for judging the muscle spasm. The muscle tension image under the muscle spasm state is collected by the muscle tension sensor, the amplitude and the frequency of the muscle tension are counted and classified according to the functions of the host and the upper computer, and the muscle tension is used as characteristic information for judging the muscle spasm. And merging and sorting the collected muscle tension data to obtain quantitative evaluation standards for evaluating muscle spasm, and recording the quantitative evaluation standards in the upper computer. During the first training scheme, the image of the muscle tension of the muscle is acquired by using the muscle tension sensor, the amplitude and the frequency of the muscle tension in the image are read, and the amplitude and the frequency are analyzed according to quantitative evaluation criteria to judge the type of muscle spasm.

The embodiment can accurately judge and judge the type of the muscle spasm, and further adjust the training parameters of the first training scheme, such as reducing the training intensity, reducing the training time and/or controlling the inflation and deflation of the elastic air bag of the electrode device to massage the spastic muscle, so as to realize timely treatment of the muscle spasm of the trainee.

Referring to fig. 6, fig. 6 is another flow chart illustrating the step S31 in fig. 2, in an embodiment, when the muscle information is the average power frequency or the median frequency of the electromyographic signal, the step S31 includes:

s314: a curve of the average or median power frequency over time is obtained.

S315: the timing is started when the average power frequency or the median frequency decreases with time.

S316: the duration of the drop in the average or median frequency is compared to a number of predetermined time ranges.

S317: a preset time range within which the average power frequency or the duration of the fall of the median frequency is determined.

S318: and determining the muscle fatigue degree corresponding to the duration of the decrease of the average power frequency or the median frequency based on the corresponding relation of the muscle fatigue degree and the preset time range.

Specifically, in this embodiment, the collected myoelectric signal is filtered, the power spectrum of the myoelectric signal is calculated, and then whether the muscle is fatigued or not is determined by calculating the average power frequency of the power spectrum of the myoelectric signal. Muscle fatigue is the inability of a muscle to continue to maintain the muscle tone and muscle contraction required or expected for exercise over a period of sustained or repeated contractions. When muscles are fatigued, metabolites such as lactic acid and the like can be accumulated in the muscles, the conduction speed of the electromyographic signals can be reduced, at the moment, the frequency spectrum curve of the electromyographic signals can be shifted left to different degrees, so that the average power frequency of the power spectrum of the electromyographic signals is reduced, and the average power frequency of the power spectrum is in a monotonous descending trend in the process of the fatiguing of the muscles. The calculation method of the power spectrum of the electromyographic signal comprises the steps of firstly calculating the discrete Fourier transform of an electromyographic signal sequence x (N) to obtain X (k), then taking the square of the amplitude of the electromyographic signal sequence x (N), and dividing the square by N to obtain the power spectrum of the electromyographic signal. The average power frequency (MPF) of the electromyographic signals is calculated after the power spectrum of the electromyographic signals is obtained.

And calculating the average power frequency of the power spectrum of the muscle electricity signal to obtain a curve of the average power frequency changing along with time, wherein the average power frequency is in an ascending trend when a user just starts training, and the average power frequency starts to decline and continuously declines along with the increase of time when muscles are fatigued. Thus, when the average power frequency decreases with time, timing is started; when the duration of the decrease of the average power frequency meets the preset condition, the muscle fatigue of the target muscle group is judged, so that the recorded muscle fatigue time is closer to the actual condition, and the accuracy of early warning is improved.

Further, the degree of fatigue of the muscle is represented by the slope value of the decrease of the average power frequency of the power spectrum of the electromyographic signals, i.e. the greater the slope value of the decrease of the average power frequency, the more severe the degree of fatigue of the muscle is represented. In addition, the muscle fatigue degree of the user doing strenuous training is more serious than that of the user doing ordinary training in the same training time. Therefore, different muscle fatigue determination conditions are set according to the slope value of the decrease of the average power frequency, and when the training is strenuous, the muscle fatigue is sent out in a short time, so that the training method is more suitable for the actual training situation, and the training safety of the user is really protected. For example, when the slope value of the decrease of the average power frequency is greater than the slope preset threshold, that is, the training intensity is high, the user may need to rest after the target muscle group is fatigued for 10 minutes, which may lead to muscle damage, and when the slope value of the decrease of the average power frequency is smaller than the slope preset threshold, that is, the training intensity is low, the user may need to rest after the target muscle group is fatigued for 20 minutes, which may set different muscle fatigue determination conditions according to the slope value of the decrease of the average power frequency, which is more suitable for the actual training situation.

By calculating the Median Frequency (MF) of the power spectrum of the muscle electricity signal, and then starting timing when the Median Frequency (MF) is increased and decreased along with time, when the duration of the decrease of the median frequency meets a preset condition, the target muscle group is judged to generate muscle fatigue.

And calculating the median frequency of the power spectrum of the muscle electricity signal to obtain a curve of the median frequency changing along with time, wherein the median frequency is in an ascending trend when the user just starts training, and the median frequency starts to decline and continuously declines along with the increase of time when the muscle is tired. Therefore, when the median frequency decreases with time, the timing is started; when the duration of the decline of the median frequency meets the preset conditions, the target muscle group is judged to generate muscle fatigue, so that the recorded muscle fatigue time is closer to the actual situation, and the accuracy of early warning is improved.

Furthermore, a judgment condition can be set according to the descending slope value of the median frequency, and when the descending slope value of the median frequency is larger than a certain preset threshold value, the muscle fatigue can send out early warning information in a short time. The embodiment sets the judgment condition according to the slope value of the reduction of the median frequency, better accords with the actual training condition, and improves the accuracy of early warning.

Referring to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of the muscle training system 100 of the present application, including: a processor 101 and an electrode device 102. The processor 101 is connected to the electrode assembly 102.

Wherein, the processor 101 is configured to obtain a first training scheme;

when a first training scheme is performed, the electrode device 102 is used for acquiring spasm fatigue information of muscles to be trained;

the processor 101 is configured to adjust the training parameters of the first training scenario according to the convulsive fatigue information to generate a second training scenario.

The electrode device 102 comprises a carrier 121 and an electrode plate 122 arranged on the outer wall of the carrier 121, wherein the carrier 121 is an elastic air bag 121;

the elastic air bag 121 is used for executing a pressure training scheme, and the electrode plate 122 is used for executing a myoelectricity training scheme;

the processor 101 is configured to analyze the spasm fatigue information to determine a muscle spasm type or a muscle fatigue degree of the muscle to be trained;

the processor 101 is configured to adjust a pressure parameter of the pressure training protocol and/or an electrical stimulation current signal parameter of the myoelectric training protocol according to a muscle spasm type or a muscle fatigue level to generate a second training protocol.

The electrode device 102 is used for acquiring myoelectric signals and muscle tension images of muscles to be trained when a user performs a first training scheme;

the processor 101 is configured to receive the electromyographic signal, and analyze the electromyographic signal and the image of the muscle tension to obtain spasm fatigue information, where the spasm fatigue information includes at least one of a myoelectricity value, a muscle tension value, a continuous active time of the electromyographic signal, a mean power frequency of the electromyographic signal, and a median frequency of the electromyographic signal.

The processor 101 is configured to filter the electromyographic signal, and draw a frequency spectrum graph based on a frequency domain and a potential graph based on a time domain of the filtered electromyographic signal;

the processor 101 is also configured to read the mean value of the amplitude in the potential map to obtain the myoelectric value, or to read the mean power frequency or median frequency in the spectrogram.

The processor 101 is configured to compare the myoelectric value with a plurality of preset myoelectric value ranges, or the continuous active time of the myoelectric signal with a plurality of preset myoelectric signal continuous active time ranges; determining a preset myoelectric value range where the myoelectric value is located, and a preset myoelectric signal continuous active time range where the myoelectric signal continuous active time is located; and determining the muscle spasm type corresponding to the myoelectricity value, the myoelectricity value or the myoelectricity signal continuous active time based on the muscle spasm type and the corresponding relation of a preset myoelectricity value range, a preset myoelectricity value range or a preset myoelectricity signal continuous active time range.

The processor 101 is configured to obtain a curve of a mean power frequency or a median frequency over time; when the average power frequency or the median frequency decreases along with the increase of time, starting timing; comparing the duration of the decrease of the average power frequency or the median frequency with a plurality of preset time ranges; determining a preset time range in which the duration of the decrease of the average power frequency or the median frequency is; and determining the muscle fatigue degree corresponding to the duration of the decrease of the average power frequency or the median frequency based on the corresponding relation of the muscle fatigue degree and the preset time range.

It should be noted that the muscle training system 100 of the present embodiment can perform the steps of the method, and the detailed description of the related contents refers to the above-mentioned method section, which is not repeated herein.

Different from the situation of the prior art, the training method and the training device have the advantages that whether the trainer has muscle spasm and/or muscle fatigue is monitored when the first training scheme is carried out, and when the trainer has muscle spasm and/or muscle fatigue, the training parameters of the first training scheme are adjusted according to spasm fatigue information, whether the trainer has muscle spasm and/or muscle fatigue can be accurately monitored, and then spasm and/or fatigue symptoms of the trainer are timely processed.

Referring to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of the muscle training apparatus 200 of the present application, wherein the apparatus 200 includes: the device comprises an acquisition unit 201, an acquisition unit 202 and a processing unit 203, wherein the processing unit 203 is respectively connected with the acquisition unit 201 and the acquisition unit 202.

The obtaining unit 201 is configured to obtain a first training scheme;

when a first training scheme is performed, the acquisition unit 202 is configured to acquire spasm fatigue information of a muscle to be trained;

the processing unit 203 is configured to adjust the training parameters of the first training scheme according to the convulsive fatigue information to generate a second training scheme.

The acquisition unit 202 is an electrode device, the electrode device includes a carrier and an electrode sheet arranged on the outer wall of the carrier, and the carrier is an elastic air bag. The elastic air bag is used for executing a pressure training scheme, and the electrode plate is used for executing a myoelectricity training scheme;

the processing unit 203 is configured to analyze the spasmodic fatigue information to determine a muscle spasmodic type or a muscle fatigue degree of the muscle to be trained;

the processing unit 203 is used for adjusting the pressure parameter of the pressure training scheme and/or the electrical stimulation current signal parameter of the myoelectric training scheme according to the muscle spasm type or the muscle fatigue degree to generate a second training scheme.

The acquisition unit 202 is configured to acquire an image of an electromyographic signal and a muscle tension of a muscle to be trained when a user performs a first training scheme;

the processing unit 203 is configured to receive the electromyographic signal, and analyze the electromyographic signal and the image of the muscle tension to obtain spasm fatigue information, where the spasm fatigue information includes at least one of a myoelectricity value, a muscle tension value, a continuous active time of the electromyographic signal, a mean power frequency of the electromyographic signal, and a median frequency of the electromyographic signal.

The processing unit 203 is configured to filter the electromyographic signal, and draw a frequency spectrum graph based on a frequency domain and a potential graph based on a time domain of the filtered electromyographic signal;

the processing unit 203 is used to read the mean value of the amplitude in the potential map to obtain the myoelectric value, or to read the mean power frequency or median frequency in the spectrogram.

The processing unit 203 is configured to compare the myoelectric value with a plurality of preset myoelectric value ranges, or the continuous active time of the myoelectric signal with a plurality of preset myoelectric signal continuous active time ranges; determining a preset myoelectric value range where the myoelectric value is located, and a preset myoelectric signal continuous active time range where the myoelectric signal continuous active time is located; and determining the muscle spasm type corresponding to the myoelectricity value, the myoelectricity value or the myoelectricity signal continuous active time based on the muscle spasm type and the corresponding relation of a preset myoelectricity value range, a preset myoelectricity value range or a preset myoelectricity signal continuous active time range.

The processing unit 203 is configured to obtain a time-varying curve of the average power frequency or the median frequency; when the average power frequency or the median frequency decreases along with the increase of time, starting timing; comparing the duration of the decrease of the average power frequency or the median frequency with a plurality of preset time ranges; determining a preset time range in which the duration of the decrease of the average power frequency or the median frequency is; and determining the muscle fatigue degree corresponding to the duration of the decrease of the average power frequency or the median frequency based on the corresponding relation of the muscle fatigue degree and the preset time range.

It should be noted that the muscle training apparatus 200 of the present embodiment can perform the steps of the method, and the detailed description of the related contents refers to the above-mentioned method section, which is not repeated herein.

Different from the situation of the prior art, the training method and the training device have the advantages that whether the trainer has muscle spasm and/or muscle fatigue is monitored when the first training scheme is carried out, and when the trainer has muscle spasm and/or muscle fatigue, the training parameters of the first training scheme are adjusted according to spasm fatigue information, whether the trainer has muscle spasm and/or muscle fatigue can be accurately monitored, and then spasm and/or fatigue symptoms of the trainer are timely processed.

Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application, a computer program 301 is stored on the computer-readable storage medium 300, and when executed by a processor, the computer program 301 implements the following steps: acquiring a first training scheme; collecting spasm fatigue information of muscles to be trained when a first training scheme is carried out; the training parameters of the first training regimen are adjusted according to the seizure fatigue information to generate a second training regimen.

In an embodiment, the step of adjusting the training parameters of the first training scheme according to the seizure fatigue information to generate the second training scheme, when the first training scheme comprises a stress training scheme and/or a myoelectric training scheme, and the computer program 301 when executed by the processor, may comprise: analyzing the spasm fatigue information to judge the muscle spasm type or muscle fatigue degree of the muscle to be trained; adjusting a pressure parameter of the pressure training regimen and/or an electrical stimulation current signal parameter according to the muscle spasm type or muscle fatigue level to generate a second training regimen.

In one embodiment, the step of collecting spastic fatigue information of the muscle to be trained when performing the first training regimen, which is performed by the processor of the computer program 301, may comprise: collecting myoelectric signals and myotension images of muscles to be trained when a user carries out a first training scheme; analyzing the myoelectric signal and the myotension image to obtain spasm fatigue information, wherein the spasm fatigue information comprises at least one of a myoelectric value, a myotension value, a continuous active time of the myoelectric signal, an average power frequency of the myoelectric signal and a median frequency of the myoelectric signal. .

In one embodiment, the step of analyzing the image of the myoelectric signal and the myotension to obtain the seizure fatigue information, implemented when the computer program 301 is executed by the processor, comprises: filtering the electromyographic signals; drawing a frequency domain-based frequency spectrum graph and a time domain-based potential graph of the filtered electromyographic signals; reading the mean value of the amplitude values in the potential diagram to obtain a myoelectric value; or reading the average power frequency or median frequency in the spectrogram.

In one embodiment, when the muscle information is the myoelectric value, the myotension value and the continuous active time of the myoelectric signal, the step of analyzing the spasm fatigue information to determine the muscle spasm type of the muscle to be trained, which is implemented when the computer program 301 is executed by the processor, includes: comparing the myoelectric value with a plurality of preset myoelectric value ranges, myoelectric tension values with a plurality of preset myoelectric tension value ranges, or myoelectric signal continuous active time with a plurality of preset myoelectric signal continuous active time ranges; determining a preset myoelectric value range where the myoelectric value is located, and a preset myoelectric signal continuous active time range where the myoelectric signal continuous active time is located; and determining the muscle spasm type corresponding to the myoelectricity value, the myoelectricity value or the myoelectricity signal continuous active time based on the muscle spasm type and the corresponding relation of a preset myoelectricity value range, a preset myoelectricity value range or a preset myoelectricity signal continuous active time range.

In one embodiment, when the muscle information is the average power frequency of the electromyographic signal, the step of analyzing the spastic fatigue information to determine the muscle fatigue degree of the muscle to be trained, which is implemented when the computer program 301 is executed by the processor, comprises: acquiring a curve of the average power frequency or the median frequency along with the change of time; when the average power frequency or the median frequency decreases along with the increase of time, starting timing; comparing the duration of the decrease of the average power frequency or the median frequency with a plurality of preset time ranges; determining a preset time range in which the duration of the decrease of the average power frequency or the median frequency is; and determining the muscle fatigue degree corresponding to the duration of the decrease of the average power frequency or the median frequency based on the corresponding relation of the muscle fatigue degree and the preset time range.

It should be noted that, when being executed by a processor, the computer program 301 of the present embodiment implements the steps of the method, and for a detailed description of related contents, refer to the above method section, which is not described in detail herein.

Different from the situation of the prior art, the training method and the training device have the advantages that whether the trainer has muscle spasm and/or muscle fatigue is monitored when the first training scheme is carried out, and when the trainer has muscle spasm and/or muscle fatigue, the training parameters of the first training scheme are adjusted according to spasm fatigue information, whether the trainer has muscle spasm and/or muscle fatigue can be accurately monitored, and then spasm and/or fatigue symptoms of the trainer are timely processed.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by the computer program 301 instructing the relevant hardware to complete, and the computer program 301 can be stored in a non-volatile computer readable storage medium, and when executed, the computer program 301 can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The above embodiments are merely examples, and not intended to limit the scope of the present application, and all modifications, equivalents, and flow charts using the contents of the specification and drawings of the present application, or those directly or indirectly applied to other related arts, are included in the scope of the present application.