CN106983511B - Method and device for identifying muscle strength and muscle tension state mutation points - Google Patents
Method and device for identifying muscle strength and muscle tension state mutation points Download PDFInfo
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Abstract
the invention discloses a method and a device for identifying a muscle strength and muscle tension state mutation point, wherein the identification method comprises the following steps: collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured; framing the collected surface electromyographic signals by utilizing a sliding window with a fixed length for the collected signals, and calculating HHT marginal spectral entropy of each frame of signals through frame shift; and calculating the number of the continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging that the starting moment of the continuous effective HHT marginal spectrum entropies is a muscle strength and muscle tension state mutation point. The method combines time-frequency domain analysis and nonlinear dynamics, and identifies the muscle strength and muscle tension mutation state points by using the set number of continuous effective HHT marginal spectrum entropies, so that the accuracy of mutation point identification can be ensured.
Description
Technical Field
The invention relates to a method and a device for identifying a muscle strength and muscle tension state mutation point.
Background
in the analysis and processing of the electromyographic signals, identifying the state mutation points of muscle strength and muscle tension (namely, the muscle strength and/or the muscle tension) can be used for intention identification, motion state monitoring, spasm detection and the like, and is a hot point of research and a difficult point of research.
The currently common electromyographic signal analyzing and processing research methods comprise a time domain method and a frequency domain method, and because the electromyographic signal is a complex physiological signal, has non-stationarity and non-linearity, and is weak and easy to interfere, the simple time domain analysis method has insufficient capacity of analyzing the electromyographic signal.
however, the conventional frequency domain analysis methods, such as FFT, are suitable for periodic signals, but are not suitable for non-periodic signals, such as myoelectric signals. At present, a time domain analysis method is mostly adopted for identifying commonly used state mutation points, the characteristic of the electromyographic signals is considered, the time domain processing method is easy to cause misjudgment, and the identification accuracy cannot be guaranteed. Although the research on electromyographic signal analysis processing has been carried out for a long time and some progress has been made in the aspect of wavelet analysis and other methods suitable for electromyographic signal analysis, the methods are still not applied to the identification of the mutation point of the muscle strength and the muscle tension state because of the problems of complex electromyographic signal, complex calculation, instantaneity, noise interference and the like.
The existing identification method of the mutation point of the muscle strength and muscle tension state is only limited to a time domain analysis method, so that the identification of the mutation point is not accurate enough, the real-time performance is difficult to guarantee, and the requirements of intention identification, spasm detection and the like cannot be better met.
Disclosure of Invention
The invention aims to provide a method for identifying a mutation point of muscle strength and muscle tension state, so as to improve the identification accuracy of the mutation point.
The invention also aims to provide a device for identifying the mutation point of the muscle strength and the muscle tension state so as to improve the identification accuracy of the mutation point.
Therefore, the invention provides a method for identifying the muscle strength and muscle tension state mutation point on one hand, which adopts a method combining a time-frequency domain analysis method and nonlinear dynamics to judge the muscle strength and muscle tension state mutation point and comprises the following steps: collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured; framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating HHT marginal spectral entropy of each frame of signals; and calculating the number of the continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective HHT marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
Further, the calculating the number of continuous effective HHT marginal spectral entropies includes: setting a self-adaptive threshold, removing the HHT marginal spectrum entropy lower than the self-adaptive threshold as an invalid marginal spectrum entropy, and keeping the rest HHT marginal spectrum entropies as an effective marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies.
Further, the calculating the number of continuous effective HHT marginal spectral entropies further comprises: the HHT marginal spectral entropy is processed prior to setting the adaptive threshold to improve sensitivity.
further, the above-described way of processing HHT marginal spectral entropy to improve sensitivity is as follows:
Wherein,For HHT marginal spectral entropy, k is the scale factor and N is the N-th power of the amplitude.
furthermore, joint angles corresponding to the electromyographic signals are synchronously acquired when the electromyographic signals are acquired; and determining a joint angle corresponding to the muscle strength and muscle tension state mutation point after judging the muscle strength and muscle tension state mutation point.
According to another aspect of the present invention, there is provided an apparatus for identifying a muscle strength and a muscle tension state mutation point, comprising a computer device including a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program: collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured; framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating HHT marginal spectral entropy of each frame of signals; and calculating the number of the continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective HHT marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
further, calculating the number of consecutive valid HHT marginal spectral entropies includes: setting a self-adaptive threshold, removing the HHT marginal spectrum entropy lower than the self-adaptive threshold as an invalid marginal spectrum entropy, and keeping the rest HHT marginal spectrum entropies as an effective marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies.
further, calculating the number of continuous effective HHT marginal spectrum entropies further comprises: the HHT marginal spectral entropy is processed prior to setting the adaptive threshold to improve sensitivity.
Further, the identification device is used for identifying a spasm starting point, wherein the muscle strength and muscle tension state mutation point is the spasm starting point.
further, the processor executes the program to further implement the following steps: synchronously acquiring joint angles corresponding to the electromyographic signals when acquiring the electromyographic signals corresponding to the muscle strength and the muscle tension to be measured; and after judging the muscle force and muscle tension state mutation point, determining a joint angle corresponding to the muscle force and muscle tension state mutation point, namely a stretch reflex threshold.
the method combines time-frequency domain analysis and nonlinear dynamics, and identifies the muscle strength and muscle tension mutation state points by using the set number of continuous effective HHT marginal spectrum entropies, so that the accuracy of mutation point identification can be ensured. Further, the sensitivity of muscle strength and muscle tension state mutation point identification is improved by processing HHT marginal spectrum entropy. The improved HHT marginal spectrum entropy solves the problem that the existing entropy calculation is complex and poor in instantaneity. Meanwhile, the detection of the angle position corresponding to the muscle force and/or the muscle tension can be realized by combining the measurement of the joint angle.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method of identifying muscle strength and muscle tension state discontinuities in accordance with the present invention;
FIG. 2 is a flowchart of an algorithm of a computer program of a method for identifying muscle strength and muscle tension state discontinuities in accordance with an embodiment of the present invention;
FIG. 3 is a graph comparing the waveform of the electromyographic signal and the rectified waveform of the HHT marginal spectral entropy corresponding thereto according to an embodiment of the present invention;
FIG. 4 is a graph comparing a waveform of an electromyographic signal and a modified HHT marginal spectral entropy rectified waveform corresponding thereto according to another embodiment of the present invention; and
Fig. 5 is a block diagram of the structure of the apparatus for identifying a muscle strength and muscle tension state mutation point according to the present invention.
Detailed Description
it should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1-5 illustrate some embodiments according to the invention.
As shown in fig. 1, the method for identifying a muscle strength and muscle tension state mutation point of the present invention includes:
S10, collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured;
When the identification method is used for intention identification and motion state monitoring, in the step, wearable acquisition equipment is preferentially adopted to acquire the electromyographic signals, for example, a wearable mobile bracelet disclosed in Chinese patent ZL201410001577.4 is worn on the upper arm or the lower arm, and then the corresponding electromyographic signals can be acquired; when the recognition method of the present invention is used for spasm detection, joint angles are detected simultaneously in addition to myoelectric signals.
S20, framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating HHT marginal spectral entropy of each frame of signals through frame shift; and
In this step, in some cases, the electromyographic signals are selectively preprocessed, and the preprocessing is mainly denoising; in other cases, no pre-processing of the acquired electromyographic signals is required.
in this step, the fixed length of the sliding window is greater than the frame shift length, for example, the fixed length of the sliding window takes 90 points, and the frame shift length takes 3 points, and the frame shift length may take other values as needed. The number of points is selected according to the calculation amount and the calculation accuracy, and for the calculation requiring small calculation amount, fewer points can be selected, otherwise, the number of points should be increased.
The HHT marginal spectrum entropy of each frame signal is calculated as follows:
Hilbert-Huang transform (HHT) is a new time-frequency analysis method, and is very suitable for processing nonlinear and non-stationary signals due to the characteristics of high adaptivity and high time-frequency resolution. The information entropy is used as a complexity index, represents the overall characteristics of the information source in an average sense and represents the average information output by the information source. For a specific information source, the larger the information entropy, the more uniform the signal component is, and the larger the uncertainty is.
The HHT consists of an Empirical Mode Decomposition (EMD) and a Hilbert transform. By EMD decomposition, subsequent Hilbert transform on each IMF component and constructing an analytical function, the signal x (t) can be expressed as:
Wherein Re is taken as the real part, ai(t)、f(t)、representing instantaneous amplitude, instantaneous frequency and instantaneous phase, respectively. The Hilbert-Huang time spectrum is defined, representing the time and frequency distribution of signal amplitudes:
time-integrating (2) to obtain Hilbert marginal spectrum of signal:
The marginal spectrum is obtained by adding the amplitudes corresponding to the specific frequencies distributed in the whole time period, and expresses the accumulation of the amplitudes of each signal frequency in the whole time period.
by definition of the margin spectrum, for a discrete frequency point f ═ i Δ f, then:
Wherein n is the number of frequency discrete points of the signal in the analysis frequency band.
According to the definition of information entropy, HHT marginal spectrum entropy can be expressed as:
In the formula, piH (i)/∑ h (i) indicates the probability that the ith frequency corresponds to the amplitude.
To normalize the entropy value within the range of [0,1], then
HHE’＝HHE/lnN------------------------------------(6)
N is the sequence length of h (i).
The waveform of the myoelectric signal of a selected subject (upper limb spasm patient) and its rectified waveform of HHT marginal spectral entropy are shown in fig. 3.
And S30, calculating the number of the continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective HHT marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
The set values are different for myoelectric signals of different parts, and the set values are generally selected to be more than or equal to 50, for example, values within the range of 50-500.
The muscle strength and muscle tension mutation state points are identified by using the set number of continuous and effective HHT marginal spectrum entropies, so that the identification indexes are quantized, and the accuracy of mutation point identification can be ensured.
in one embodiment, calculating the number of consecutive valid HHT marginal spectral entropies comprises: setting a self-adaptive threshold, removing the HHT marginal spectrum entropy lower than the self-adaptive threshold as an invalid marginal spectrum entropy, and keeping the rest HHT marginal spectrum entropies as an effective marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies. In this embodiment, the identification of muscle tension discontinuities can be quantified, avoiding the subjective randomness of state discontinuity identification.
In one embodiment, the electromyographic signal processing algorithm is as shown in fig. 2: and performing 90-point sliding window framing on the electromyographic signals, moving the frames to 3 points, and calculating the HHT marginal spectrum entropy value of each frame of signals and recording the value as MSEn. And then setting an adaptive threshold value to rectify the marginal spectrum entropy to obtain En, setting the MsEn value lower than Th to be 0, and keeping the MsEn value larger than Th. When the En value rectified at a certain moment is greater than 0 and the En values of the subsequent 50 En values are greater than 0, the moment is judged to generate the muscle tension catastrophe point. This embodiment gives an example of the application of the present identification method to the identification of the onset of a spasm, i.e., the identification of the onset of a spasm using the sEMG signal of the biceps brachii.
Wherein the adaptive threshold Th is determined according to the following formula:
Th＝min(MsEn)+λ[max(MsEn)-min(MsEn)]---------------------(7)
In one embodiment, min (msen) is the minimum value of HHT marginal spectral entropy in all frames, [ max (msen) is the minimum value of HHT marginal spectral entropy in all frames, and λ is 0.3.
Although there is a certain difference between the muscle strength and the entropy of the electromyographic signals before and after the muscle tension state mutation point, the entropy after setting the threshold value and rectifying appears a point which is not 0 continuously for many times in the base signal before the state mutation point, and the accuracy of the stretch reflex electromyographic threshold value judgment is influenced. In order to find out an index which can more obviously reflect the change of the signals before and after the mutation point of the muscle strength and the muscle tension state and improve the accuracy of the identification algorithm, the method for calculating the entropy value is correspondingly improved, so that the difference of the entropy values of the signals before and after the mutation point is more obvious.
in a further embodiment, calculating the number of consecutive valid HHT marginal spectral entropies comprises: firstly, processing HHT marginal spectrum entropy to improve sensitivity; setting a self-adaptive threshold, removing the processed HHT marginal spectrum entropy lower than the self-adaptive threshold as an invalid marginal spectrum entropy, and keeping the rest of the processed HHT marginal spectrum entropy as an effective marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies.
analyzing the characteristics of the HHT marginal spectrum entropy, and because of normalization and other reasons, the entropy value is finally limited in the range of (0,1), and the difference between the front and the back of the signal is difficult to be obviously described, based on the characteristics, the thought of utilizing the amplitude value to the power of N and a scale factor k is provided, normalization is removed, and the calculation method of the HHT marginal spectrum entropy is improved to improve the sensitivity of muscle strength and muscle tension state mutation point identification:
Analyzing the range of entropy values and the experimental result, the experiment determines that the factor coefficient N is 2, and k is 0.5, and the effect is best.
The waveform of the same electromyographic signal and the waveform of the improved HHT marginal spectrum entropy (the waveform is rectified) are shown in FIG. 4, compared with FIG. 3 and FIG. 4, the difference of the entropy values of the electromyographic signals before and after the state mutation point P is large and remarkable, the entropy value after setting the threshold value for rectification is almost all 0 in the base signal, and the accuracy and the sensitivity of the interpretation threshold value are improved by the improved algorithm.
As shown in fig. 5, the apparatus for identifying a muscle strength and muscle tension state mutation point according to the present invention comprises: the computer equipment comprises a memory 31, a processor 32, a display 33, a computer program which is stored in the memory 31 and can run on the processor 32, and the computer equipment further comprises a surface electromyogram signal acquisition device 20, wherein the surface electromyogram signal acquisition module comprises a surface electromyogram signal sensor and an electromyogram signal processing module, and the electromyogram signal processing module comprises a conditioning circuit which is electrically connected with an angle sensor, an A/D conversion module which is connected with the conditioning circuit, and a signal sending module which is connected with the A/D conversion module.
The processor 32, when executing the program, implements the steps of: s10, collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured; s20, framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating HHT marginal spectral entropy of each frame of signals through frame shift; and S30, calculating the number of the continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective HHT marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
The identification method and the identification device can also be used for identifying the spasm starting point (spasm mutation point).
Spasm assessment plays a crucial role in clinical and scientific research, such as the formulation of rehabilitation schemes, the adjustment of anti-spasm drug dosage and the like, and doctors are required to make objective and accurate assessment on muscle tension. The existing clinical commonly-used grading type spasm Scale is MAS (Modified Ashworth Scale), the evaluation result mainly depends on subjective judgment of an evaluator, the grading description of the Scale for the spasm grade is fuzzy, the Scale belongs to semi-quantitative description, the spasm condition is difficult to accurately reflect, data is difficult to store, and objective, accurate and quantitative evaluation requirements cannot be met.
The electromyographic signals have been documented to be effective in characterizing stretch reflex thresholds. However, the existing stretch reflex threshold detection method based on sEMG signals is mainly based on an artificial vision method and a time domain related parameter method. The artificial vision method detects sEMG change points through vision, is high in subjectivity and cannot meet the requirement for accurate and objective spasm evaluation. In addition, because of the electromyographic signals of different position movement units, different phases and amplitudes are shown due to different time spent by the action potential reaching the acquisition point, and the signals with different phases and amplitudes are disorderly superposed together to ensure the non-stationarity of the sEMG. At the same time, the researchers have pointed out that the motor nerve units involved in muscle activity show differences in the number, number and conduction rate of discharges in different action modes and states of motion of muscles, and these phenomena are called changes in the motor complexity in terms of dynamics. And the electromyographic signals are weak and easy to interfere, so that the capacity of analyzing the electromyographic signals by a simple time domain and frequency domain analysis method is insufficient.
Therefore, the invention provides a spasm measurement evaluation device for judging the dynamic stretch reflex threshold value by using the HHT marginal spectrum entropy and the joint motion angle of the related muscle sEMG.
As shown in fig. 5, the identification device of the present invention is combined with an angle acquisition device 10 (for acquiring the joint angle of the affected elbow joint of the patient during passive extension movement), so as to determine the stretch reflex threshold (defined as the joint angle corresponding to the muscle tension abrupt change point of the flexor spasm patient) based on the surface myoelectric HHT marginal spectrum entropy. The angle acquisition device comprises an angle sensor and a signal processing module, wherein the signal processing module comprises a conditioning circuit electrically connected with the angle sensor, an A/D conversion module connected with the conditioning circuit and a signal sending module connected with the A/D conversion module.
wherein the processor 32 implements the following steps when executing the program: obtaining joint angle and biceps brachii surface myoelectric signals in the passive extension motion process of the elbow joint of the affected side of the tested person from the maximum angle of flexion to the maximum angle of extension; framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating HHT marginal spectral entropy of each frame of signals through frame shift; calculating the number of continuous effective HHT marginal spectrum entropies, and if the number of the continuous effective HHT marginal spectrum entropies is larger than a set value, judging the starting moment of the continuous effective HHT marginal spectrum entropies as a spasm starting point; and finding out the joint angle value corresponding to the spasm starting point to obtain the stretch reflex threshold.
According to the embodiment of the invention, the measurement of the stretch reflex threshold of the upper limb flexor spasm patient is realized. The method comprises the following specific steps:
the acquisition of the sEMG signal of the biceps brachii adopts three-point differential input. After the skin is wiped by alcohol to remove grease and dandruff on the surface of the skin, two electrode plates serving as differential input ends of myoelectricity are placed at the belly of a muscle along the direction of muscle fibers, and the other electrode plate is taken as a reference ground and placed at a place without muscle activity. The centers of the two electrodes are separated by 20 mm. The lead wire is properly fixed, and the interference of the lead wire shaking in the action process is reduced as much as possible.
The electrode slice is arranged and is accomplished the back examiner and dresses angle acquisition module at the sick side upper limbs elbow joint of examinee, ensures that angle sensor's axle center is unanimous with elbow joint center upper arm bone external epicondyle in the angle acquisition module, and the while is fixed the bandage and is guaranteed the motion in-process device and can not become flexible to guarantee that device rotation axis and elbow joint rotation axis are coaxial. Meanwhile, the angle adjusting acquisition device ensures that the angle adjusting acquisition device is not interfered with the myoelectricity acquisition device.
During the test, the testee takes a sitting posture neutral position, and the examiner scores the upper limb MAS of the testee at a proper speed according to experience. Subjects were asked to assume a sitting position to ensure that they were relaxed for evaluation.
in the detection process, each sensor processes the detected data through a conditioning circuit, performs analog-to-digital conversion on the processed data and sends the processed data to computer equipment, and the computer equipment can display the elbow joint angle change condition and the elbow joint angular velocity condition in the upper limb movement process and the biceps brachii surface myoelectric signals in real time and stores the data in real time.
thirdly, after elbow joint angles and surface electromyogram data are obtained through the method, the stretch reflex threshold is judged according to HHT marginal spectrum entropy of the sEMG of the biceps brachii, and the judging method is shown in fig. 2. And performing 90-point sliding window framing on the original electromyographic signals or the electromyographic signals subjected to amplitude zero equalization processing, moving the frames to 3 points, and calculating HHT marginal spectrum entropy of each frame of signals and recording the HHT marginal spectrum entropy as MSEn. And then setting an adaptive threshold value to rectify the marginal spectrum entropy to obtain En, setting the MsEn value lower than Th to be 0, and keeping the MsEn value larger than Th. When the En value rectified at a certain moment is greater than 0 and the En values of the subsequent 50 En values are greater than 0, the moment is judged to generate the muscle tension catastrophe point.
It should be noted that, for some surface electromyogram signal acquisition devices, zero equalization processing is required to be performed on the acquired raw electromyogram signal, and the processing only adjusts the amplitude integrally, and has no influence on the waveform.
And (IV) finding out the joint angle corresponding to the muscle tension abrupt change point, namely obtaining the stretch reflex threshold.
the method realizes the judgment of the stretch reflex threshold value through the combination of the surface electromyogram data and the joint angle. The quantitative evaluation of the spasm is realized, and the problem of large subjectivity of the current spasm detection is solved. The method is also suitable for the assessment of upper limb extensor spasm, wrist spasm and lower limb knee joint and ankle spasm.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for identifying a muscle strength and muscle tension state mutation point is characterized in that the muscle strength and muscle tension state mutation point is judged by adopting a method combining a time-frequency domain analysis method and nonlinear dynamics, and the method comprises the following steps:
collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured;
Framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating the Hilbert-Huang transform marginal spectral entropy of each frame of signals through frame shift; and
And calculating the number of the continuous effective Hilbert-Huang transform marginal spectrum entropies, and if the number of the continuous effective Hilbert-Huang transform marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective Hilbert-Huang transform marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
2. The method for identifying muscle strength and muscle tension state mutation points according to claim 1, wherein calculating the number of consecutive effective hilbert-yellow transform marginal spectral entropies comprises: setting a self-adaptive threshold, removing the Hilbert-Huang transform marginal spectrum entropy lower than the self-adaptive threshold as invalid marginal spectrum entropy, and keeping the rest Hilbert-Huang transform marginal spectrum entropy as valid marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies.
3. The method for identifying muscle strength and muscle tension state mutation points according to claim 2, wherein calculating the number of consecutive effective hilbert-yellow transform marginal spectral entropies further comprises: the hilbert-yellow transform marginal spectral entropy is processed prior to setting the adaptive threshold to improve sensitivity.
4. The method for identifying a muscle strength and tension state mutation point as claimed in claim 3, wherein the marginal spectral entropy of the Hilbert-Huang transform is processed to improve the sensitivity in the following way:
wherein,Is the marginal spectral entropy of Hilbert-Huang transform, k is a scale factor, N is the power of N of the amplitude, piindicating the probability that the ith frequency corresponds to the amplitude.
5. The method for identifying the muscle strength and muscle tension state mutation point according to claim 1, wherein joint angles corresponding to the electromyographic signals are synchronously collected when the electromyographic signals are collected; and determining a joint angle corresponding to the muscle strength and muscle tension state mutation point after judging the muscle strength and muscle tension state mutation point.
6. an apparatus for identifying muscle strength and muscle tension state discontinuities, comprising a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of:
Collecting myoelectric signals corresponding to the muscle strength and the muscle tension to be measured;
Framing the collected surface electromyographic signals by using a sliding window with a fixed length, and calculating the Hilbert-Huang transform marginal spectral entropy of each frame of signals through frame shift; and
And calculating the number of the continuous effective Hilbert-Huang transform marginal spectrum entropies, and if the number of the continuous effective Hilbert-Huang transform marginal spectrum entropies is larger than a set value, judging that the starting time of the continuous effective Hilbert-Huang transform marginal spectrum entropies is a muscle strength and muscle tension state mutation point.
7. The apparatus for identifying muscular strength and muscular tension state mutation points as claimed in claim 6, wherein calculating the number of consecutive effective Hilbert-Huang transform marginal spectral entropies comprises: setting a self-adaptive threshold, removing the Hilbert-Huang transform marginal spectrum entropy lower than the self-adaptive threshold as invalid marginal spectrum entropy, and keeping the rest Hilbert-Huang transform marginal spectrum entropy as valid marginal spectrum entropy; and calculating the continuous number of the effective marginal spectrum entropies.
8. The apparatus for identifying muscle strength and muscle tension state mutation points as claimed in claim 7, wherein calculating the number of consecutive effective Hilbert-Huang transform marginal spectral entropies further comprises: the hilbert-yellow transform marginal spectral entropy is processed prior to setting the adaptive threshold to improve sensitivity.
9. The apparatus for identifying a muscle strength and muscle tension state transition point according to claim 6, wherein the apparatus is configured to identify a spasm onset point, wherein the muscle tension state transition point is a spasm onset point.
10. The apparatus for identifying a muscle strength and tension state discontinuity according to claim 9, wherein said processor when executing said program further performs the steps of: synchronously acquiring joint angles corresponding to the electromyographic signals when acquiring the electromyographic signals corresponding to the muscle strength and the muscle tension to be measured; and after judging the muscle strength and muscle tension state mutation point, determining a joint angle corresponding to the muscle tension state mutation point, namely a stretch reflex threshold.
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