CN111973183A - Joint measurement device and method for muscle fatigue and artificial limb - Google Patents
Joint measurement device and method for muscle fatigue and artificial limb Download PDFInfo
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Abstract
A joint measurement device and prosthesis of muscle fatigue, comprising: the power supply module is used for supplying power; the flexible deformation sensor is used for detecting a deformation signal of the detected muscle; the Ag/AgCl gel electrode is used for detecting a surface electromyographic signal of the detected muscle; and the processing module is electrically connected with the deformation sensor and the Ag/AgCl gel electrode, and is used for carrying out feature extraction on the deformation signal and the surface electromyographic signal to obtain a feature set and obtaining muscle state data representing the fatigue state of the current measured muscle according to the feature set. The flexible deformation sensor is used for acquiring the deformation signal of the muscle in real time, the Ag/AgCl gel sensor is used for acquiring the surface electromyographic signal in real time, the fatigue state of the muscle is obtained, high-quality measurement data is provided, errors are reduced, and the precision is improved.
Description
Technical Field
The invention belongs to the field of intelligent medical equipment, and particularly relates to a muscle fatigue joint measurement device and method.
Background
The intelligent artificial limb is an artificial limb which utilizes the nerve signals (myoelectricity, electroencephalogram and peripheral nerve electric signals) of the human body to identify the movement intention of the human body for action control, and can more conveniently help the limb handicapped patients to blend into the daily life. At present, the control precision and stability of the intelligent artificial limb still have some problems, and the control precision and stability are far away from the real limb function, so that the comfort and the experience degree are poor. One important reason is that the intelligent artificial limb is easily affected by muscle fatigue in the long-term control process, so that the intelligent artificial limb needs to be trained before the muscle fatigue regularly, and how to judge that the muscle is tired is a precondition for performing the regular training.
At present, the method for judging muscle fatigue usually judges whether muscle is fatigue or not through subjective feeling of a human body and visual observation of whether an intelligent artificial limb can complete a set task so as to carry out periodic training. In addition, a method for detecting the progress of muscle fatigue by using the entropy of the ultrasonic image is also provided.
Several current methods of determining muscle fatigue suffer from different drawbacks. For the subjective judgment method, the subjective consciousness of a person is often influenced by factors such as emotion, psychological activity, environment and the like, whether muscles are tired or not is subjectively judged without objectivity, and the error is large due to the influence of the difference of the person; for the visual judgment method, the intelligent artificial limb is visually monitored, and data acquisition and judgment are carried out according to the task completion degree, so that the method is difficult and inconvenient to operate; the electromyography judgment method is characterized in that electromyography signals on the surface of muscles are collected and analyzed, the data collection mode is single, physical signals of limbs when muscle fatigue occurs are ignored, the analysis method is simple, and the accuracy is low. And in addition, the measurement process of the ultrasonic imaging entropy judgment method is complex, the ultrasonic imaging entropy judgment method is inconvenient to carry, and special ultrasonic imaging equipment is required.
Disclosure of Invention
The invention aims to provide a combined measurement device and method for muscle fatigue, and aims to solve the problems of inconvenience and low precision of the traditional muscle fatigue measurement method.
A first aspect of an embodiment of the present invention provides a joint measurement apparatus for muscle fatigue, including:
the power supply module is used for supplying power;
the flexible deformation sensor is used for detecting a deformation signal of the detected muscle;
the Ag/AgCl gel electrode is used for detecting a surface electromyographic signal of the detected muscle; and
and the processing module is electrically connected with the flexible deformation sensor and the Ag/AgCl gel electrode, and is used for performing characteristic extraction on the deformation signal and the surface electromyographic signal to obtain a characteristic set and obtaining muscle state data representing the fatigue state of the current measured muscle according to the characteristic set.
In one embodiment, the processing module is specifically configured to:
preprocessing the deformation signal and the surface electromyogram signal;
extracting a root mean square value of the sampling points of the preprocessed deformation signals, and an integral myoelectric value, an average power frequency and a median frequency of the preprocessed surface myoelectric signals to form a feature set; and
And obtaining muscle state data representing the current tested muscle fatigue state according to the feature set.
In one embodiment, the muscle state monitoring device further comprises a feedback module connected with the processing module, the processing module is further configured to output a corresponding control signal according to the muscle state data, and the control signal is used to control the feedback module to generate indication information matched with the current muscle state.
In one embodiment, the feedback module includes at least one of a speaker, an indicator light, and a vibrator.
In one embodiment, the muscle state data processing device further comprises a storage module, wherein the storage module is connected with the processing module and used for storing the muscle state data.
In one embodiment, the muscle state monitoring system further comprises a communication module, wherein the communication module is connected with the processing module and is used for transmitting the muscle state data.
A second aspect of the embodiments of the present invention provides a method for jointly measuring muscle fatigue, including:
detecting a deformation signal of the detected muscle by using a flexible deformation sensor and detecting a surface electromyogram signal of the detected muscle by using an Ag/AgCl gel electrode;
carrying out feature extraction on the deformation signal and the surface electromyogram signal to obtain a feature set;
And obtaining muscle state data representing the current tested muscle fatigue state according to the feature set.
In one embodiment, the extracting the characteristics of the deformation signal and the surface electromyogram signal to obtain a characteristic set includes:
preprocessing the deformation signal and the surface electromyogram signal;
and extracting the root mean square value of the sampling point number of the preprocessed deformation signal, and the integral myoelectric value, the average power frequency and the median frequency of the preprocessed surface myoelectric signal to form a feature set.
In one embodiment, the method further comprises the following steps:
generating a corresponding control signal according to the muscle state data;
and controlling a feedback module to generate indication information matched with the current muscle state by using the control signal.
A third aspect of an embodiment of the invention provides a prosthesis comprising a joint measurement of muscle fatigue as described above.
According to the muscle fatigue joint measurement device and method, the flexible deformation sensor is used for acquiring the deformation signal of the muscle in real time, the Ag/AgCl gel sensor is used for acquiring the surface electromyographic signal in real time, the physical signal and the physiological electric signal of the muscle are jointly measured, and the muscle fatigue state is obtained, so that high-quality joint measurement data can be provided, errors are reduced, and the precision is improved; meanwhile, the combined measuring device can measure the muscle state in a real-time and non-invasive mode, and is small in size, light in weight and convenient to use.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic structural diagram of a combined measurement device for muscular fatigue according to a first embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a combined measurement device for muscular fatigue according to a second embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for measuring muscle fatigue in a combined manner according to a first embodiment of the present invention;
FIG. 4 is a detailed flowchart of step S120 of the joint measurement method for muscle fatigue shown in FIG. 1;
fig. 5 is a specific flowchart of a muscle fatigue joint measurement method according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Human motion intention recognition is one of the key technologies of intelligent artificial limbs. Accurate movement intention identification can improve the control performance of the artificial limb and improve the use experience of a user. The signals commonly used for the identification of the motor intention are physiological electrical signals and mechanical signals. Among physiological signals, the surface electromyogram signal is one of the most widely used signals in the extraction of motor intentions because it is non-invasive and safe. But the surface electromyogram signal is weak and is easily interfered by the muscle fatigue of the human body. And the intelligent artificial limb needs to be trained and parameter corrected regularly in long-term use so as to maintain the control accuracy of the intelligent artificial limb. Meanwhile, in the received interference, the generation of muscle fatigue is the original interference, and the specific result can cause the disorder of physiological electric signals, so that the control is wrong, and in the actual use, operation accidents can happen under severe conditions. The training and data correction of the intelligent artificial limb is very important before the muscle fatigue occurs.
Referring to fig. 1, according to the joint measurement device 100 for muscle fatigue of an artificial limb provided by the embodiment of the present invention, a deformation signal and a Surface Electromyography (Surface Electromyography) signal generated by a measured muscle are recorded by a muscle deformation flexible sensor and an Ag/AgCl gel electrode, and are transmitted to a processing module in a multi-channel AD sampling manner, and a machine learning algorithm is combined to determine a current state of the muscle, and then the result is transmitted to a feedback module, and a wearer is prompted to perform intent recognition training before muscle fatigue by stimulation manners such as voice, light, vibration, and the like, so that an intelligent artificial limb can be ensured to avoid unstable control caused by muscle fatigue to the greatest extent.
In this embodiment, the joint measurement device 100 for muscular fatigue includes a power module 110, a flexible deformation sensor 120, an Ag/AgCl gel electrode 130, and a processing module 140.
The power module 110 is used to supply power. In some embodiments, the power module 110 is composed of a battery and a regulator circuit, wherein the battery provides a voltage to the regulator circuit, and the regulator circuit provides a stable DC operating voltage to the joint measuring device 100.
The flexible deformation sensor 120 generally winds around the muscle to be detected for one circle, and detects the deformation signal of the muscle to be detected, and the technical scheme disclosed in the patent application number CN201810972614.4 can be adopted; the Ag/AgCl gel electrode 130 is directly contacted with the muscle to be detected, and detects the surface electromyographic signal of the muscle to be detected.
The processing module 140 is generally a single chip microcomputer, the processing module 140 is electrically connected to the flexible deformation sensor 120 and the Ag/AgCl gel electrode 130, the processing module 140 receives the deformation signal and the surface electromyogram signal transmitted from the analog-to-digital converter, performs feature extraction on the deformation signal and the surface electromyogram signal to obtain a feature set, and obtains muscle state data representing the fatigue state of the currently measured muscle according to the feature set.
Specifically, the processing module 140 pre-processes the deformation signal and the surface electromyogram signal, and then extracts the time domain characteristics of the pre-processed deformation signal; windowing is carried out on the surface electromyographic signals according to the time domain and frequency domain characteristics of the signals, and integral electromyographic values, average power frequencies and median frequencies of the surface electromyographic signals are extracted to form characteristic sets; and finally, obtaining muscle state data representing the current tested muscle fatigue state according to the feature set and outputting the muscle state data. The physical signal and the physiological electric signal of the muscle are jointly measured to obtain the fatigue state of the muscle, so that high-quality joint measurement data can be provided, errors are reduced, and the precision is improved. The number of the flexible deformation sensor 120 and the number of the Ag/AgCl gel electrodes 130 are one or more, and multi-channel measurement can be realized when the number of the flexible deformation sensor 120 and the number of the Ag/AgCl gel electrodes 130 are more than one, so that the measurement precision is further improved.
Specifically, for the deformation signal, the device can acquire the signal at a lower sampling rate (50 Hz-200 Hz), so that the calculation requirement is low, and the processing effect of real-time processing (300ms) can be achieved. The preprocessing of the deformation signal comprises the following steps: removing direct current components, processing action artifacts, processing overcharge points, extracting and dividing initial states and the like.
Furthermore, during muscle fatigue, the muscle may vibrate significantly, the degree of synchronization of the motion unit may change, and the signal may be used as an index of the fatigue state by performing feature extraction. In this scheme, RMS (root mean square value) of the number of sampling points of the muscle by the flexible deformation sensor 120 is extracted as a characteristic threshold, and the change of the characteristic threshold mainly reflects the number of activated motion units during muscle activity, the types of motion units participating in the activity and the degree of synchronization. The RMS calculation method comprises the following steps:
Specifically, for the electromyographic signals, raw data is collected (0.5KHz to 2KHz) at a high sampling rate, a plurality of sensors are used for collecting the electromyographic signals at a multichannel position, in a hardware part, the raw signals are subjected to preliminary noise reduction processing in a hardware filtering mode, and the raw data are uploaded to the processing module 140 in real time through WIFI or Bluetooth in a data framing and packaging mode. The processing module 140 performs secondary processing on the signal by using a digital signal processing method. The method comprises the preprocessing processes of removing direct current components, removing motion artifacts, removing power frequency (50 Hz) signal interference in continental regions, processing overcharge point signals, reducing Wavelet Transform (WT) noise and the like.
For electromyographic signals, due to the time-varying and non-stationary characteristics of biological signals, various methods are required to extract the fatigue characteristics. The windowing method can divide a long signal into interested short signals, perform feature extraction on the short signals of each section, finally synthesize all features of the windowed signals to obtain a large feature set, and extract main features by a Principal Component Analysis (PCA) dimension reduction method to achieve maximum discrimination. For the extracted integrated myoelectric value (IEMG), Median Frequency (MF) and Mean Power Frequency (MPF) features. Wherein, the IEMG can reflect the total muscle discharge amount of all the participated activities in a certain time, and the characteristics can obviously change along with the increase of the fatigue degree in the process of muscle fatigue. The MF may reflect the power spectrum result of the fast fourier transform of all the myoelectric signals participating in the activity at a certain point in time. The MPF may reflect a frequency value corresponding to an average value of the power of the moving unit at a certain time point. They can be used to express biological indicators of the median of the discharge frequency of the motor unit and the signal frequency characteristics.
IEMG calculation method:
wherein: n1 is the start of integration, N2 is the end of integration, x (t) is the electromyogram, and dt is the time interval between samples.
The MF calculation method comprises the following steps:
wherein: psd (f) is a function of the power spectral density of the surface electromyogram signal.
The MPF calculation method comprises the following steps:
wherein: psd (f) is a function of the power spectral density of the surface electromyogram signal.
And obtaining muscle state data representing the current tested muscle fatigue state according to the feature set. Specifically, in the present embodiment, the muscle state is mainly obtained according to the feature set by using a machine learning algorithm. The fatigue state is determined by the characteristics using an algorithm such as LDA (Linear Discriminant Analysis), SVM (Support Vector Machine), BP (Back Propagation), KNN (K-nearest neighbor, K-means classifier), etc. as a Machine learning algorithm. For example, different result weights can be given to the four algorithms, and whether fatigue occurs can be determined by comparing the weight values of the four results with a fatigue threshold.
Referring to fig. 2, in one embodiment, the joint measurement device 100 for muscle fatigue further includes a feedback module 150, the feedback module 150 is connected to the processing module 140, and the processing module 140 is further configured to output a corresponding control signal according to the muscle state data, where the control signal is used to control the feedback module 150 to generate indication information matched with the current muscle state. The feedback module 150 includes at least one of a speaker, an indicator light, and a vibrator. Thus, the control signal can trigger voice, light source, vibration and the like to provide feedback muscle fatigue early warning for the wearer, and training reminding is carried out before the stability of the equipment (such as a prosthesis) needing training is disturbed. Therefore, the combined measuring device 100 can measure the muscle state in a real-time and non-invasive manner, has small volume and light weight, is convenient to use, and can improve the wearing comfort and the wearing stability of the artificial limb.
In one embodiment, the joint measurement apparatus 100 further includes a storage module 160, and the storage module 160 is connected to the processing module 140 for storing the muscle state data. Further, the joint measurement device 100 further includes a communication module 170, and the communication module 170 is connected to the processing module 140 for transmitting the muscle state data. The communication module 170 is WIFI or bluetooth, and transmits data to the cloud end to record and call the historical data of the testee, and also can form the behavior habit of the testee according to the historical data.
Referring to fig. 3, a second aspect of the embodiment of the present invention provides a method for joint measurement of muscle fatigue, including the following steps:
step S110, acquiring a deformation signal of the muscle to be detected by using the flexible deformation sensor 120 and acquiring a surface electromyogram signal of the muscle to be detected by using the Ag/AgCl gel electrode 130;
step S120, extracting the characteristics of the deformation signal and the surface electromyogram signal to obtain a characteristic set;
and S130, obtaining muscle state data representing the fatigue state of the currently measured muscle according to the feature set.
Referring to fig. 4, in one embodiment, the step S120 of extracting the characteristics of the deformation signal and the surface electromyogram signal to obtain a characteristic set includes:
Step S121, preprocessing including artifact removal, filtering and noise reduction is carried out on the deformation signal and the surface electromyogram signal;
and S122, extracting the time domain characteristics of the preprocessed deformation signals, and the integral electromyographic values and the median frequency of the preprocessed surface electromyographic signals to form a characteristic set.
Referring to fig. 5, in one embodiment, the method for jointly measuring muscle fatigue further includes:
step S130, generating a corresponding control signal according to the muscle state data;
in step S140, the feedback module 150 is controlled by the control signal to generate indication information matching with the current muscle state.
The device 100 and the method for the joint measurement of muscle fatigue obtain the deformation signal of the muscle in real time by using the flexible deformation sensor 120 and obtain the surface electromyographic signal in real time by using the Ag/AgCl gel sensor, and perform joint measurement on the physical signal and the physiological electric signal of the muscle to obtain the muscle fatigue state, if high-quality joint measurement data can be provided, the error is reduced, and the precision is improved; meanwhile, the combined measuring device 100 can measure the muscle state in a real-time and non-invasive manner, and is small in size, light in weight and convenient to use.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A joint measurement device of muscle fatigue, comprising:
the power supply module is used for supplying power;
the flexible deformation sensor is used for detecting a deformation signal of the detected muscle;
the Ag/AgCl gel electrode is used for detecting a surface electromyographic signal of the detected muscle; and
and the processing module is electrically connected with the flexible deformation sensor and the Ag/AgCl gel electrode, and is used for performing characteristic extraction on the deformation signal and the surface electromyographic signal to obtain a characteristic set and obtaining muscle state data representing the fatigue state of the current measured muscle according to the characteristic set.
2. The system of claim 1, wherein the processing module is specifically configured to:
preprocessing the deformation signal and the surface electromyogram signal;
extracting a root mean square value of the sampling points of the preprocessed deformation signals, and an integral myoelectric value, an average power frequency and a median frequency of the preprocessed surface myoelectric signals to form a feature set; and
And obtaining muscle state data representing the current tested muscle fatigue state according to the feature set.
3. The system of claim 1, further comprising a feedback module connected to the processing module, wherein the processing module is further configured to output a corresponding control signal according to the muscle state data, and wherein the control signal is configured to control the feedback module to generate an indication matching a current muscle state.
4. The system of claim 3, wherein the feedback module comprises at least one of a speaker, an indicator light, and a vibrator.
5. The system of claim 1, further comprising a storage module coupled to the processing module for storing the muscle state data.
6. The system of claim 1, further comprising a communication module coupled to the processing module for transmitting the muscle state data.
7. A method for joint measurement of muscle fatigue, comprising:
detecting a deformation signal of the detected muscle by using a flexible deformation sensor and detecting a surface electromyogram signal of the detected muscle by using an Ag/AgCl gel electrode;
Carrying out feature extraction on the deformation signal and the surface electromyogram signal to obtain a feature set;
and obtaining muscle state data representing the current tested muscle fatigue state according to the feature set.
8. The joint measurement method of muscle fatigue according to claim 7, wherein the feature extraction of the deformation signal and the surface electromyography signal to obtain a feature set comprises:
preprocessing the deformation signal and the surface electromyogram signal;
and extracting the root mean square value of the sampling point number of the preprocessed deformation signal, and the integral myoelectric value, the average power frequency and the median frequency of the preprocessed surface myoelectric signal to form a feature set.
9. The joint measurement method of muscle fatigue according to claim 7, further comprising:
generating a corresponding control signal according to the muscle state data;
and controlling a feedback module to generate indication information matched with the current muscle state by using the control signal.
10. A prosthesis comprising a combined measurement of muscle fatigue as claimed in any one of claims 1 to 6.
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