CN111714123A - System and method for detecting human body waist and back surface electromyographic signals - Google Patents
System and method for detecting human body waist and back surface electromyographic signals Download PDFInfo
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- CN111714123A CN111714123A CN202010711501.6A CN202010711501A CN111714123A CN 111714123 A CN111714123 A CN 111714123A CN 202010711501 A CN202010711501 A CN 202010711501A CN 111714123 A CN111714123 A CN 111714123A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
The invention discloses a system and a method for detecting the electromyographic signals of the surface of the waist and the back of a human body, wherein the system comprises the following components: the acquisition unit and the signal processing terminal are connected in sequence; the acquisition unit is used for acquiring the electromyographic signals of the waist and back surfaces of the human body of the subject and sending the electromyographic signals to the signal processing terminal; the signal processing terminal comprises a digital filtering module, a signal rectifying module, an activity section detection module, a feature extraction module and a muscle fatigue degree analysis module and is used for processing the surface muscle electrical signals to form visual chart parameters; the invention carries out secondary filtering on the electromyographic signals. The digital filtering module of the signal processing terminal filters the collected electromyographic signals for the first time, removes noise interference outside an effective frequency range of the electromyographic signals and removes power frequency interference of 50 Hz; and filtering the signal for the second time by using a digital filtering module of the signal processing terminal to remove part of noise which cannot be filtered by a hardware circuit and improve the signal-to-noise ratio.
Description
Technical Field
The invention relates to the technical field of bioelectricity acquisition and control, in particular to a system and a method for detecting a human body waist and back surface electromyographic signal.
Background
The electromyographic signals are the result of superposition of action potentials on the skin surface of a human body caused by contraction of dominant muscle fibers when an active motor unit of the central nervous system of the human body is excited. The surface electromyogram signal is an electromyogram signal obtained by taking a surface electrode as a guide for measurement, does not cause human body damage during measurement, is easy to operate, and is convenient and advisable when applied to experimental tests. The surface electromyogram signal technology is widely applied to the fields of clinical medicine, rehabilitation medicine, sports science, human engineering and the like, provides accurate and reliable information for the activity of a neuromuscular system in various states, and becomes an objective basis for the evaluation and research of the neuromuscular function.
Among the various musculoskeletal diseases, low back pain is the most common. Lumbago is a troublesome problem which troubles industrialized countries and workers, and along with the acceleration of industrialized progress and the acceleration of population aging, the threat of lumbago and backache to health is increasing day by day. Therefore, it is necessary to research and develop a method for monitoring the muscle fatigue degree of a human body in time by specially collecting the myoelectric signals of the muscles of the back and the waist.
Most of the current acquisition devices applied to surface electromyogram signal testing acquire data through sampling electrodes adhered to the surface of the skin, and the acquired data are transmitted to a computer for manual analysis, so that the following defects exist:
(1) the electrodes are not convenient to paste, not firm to fix and limited in application environment, and are easy to shift when a human body is in a motion state; the discomfort of the tested person is easily caused.
(2) The collection process is easy to introduce larger power frequency interference and environment interference, the noise filtering circuit cannot completely remove noise, and the collected electromyographic signals are low in signal-to-noise ratio and difficult to acquire a real muscle activity state.
(3) The oscillogram formed by the electromyographic signals acquired by a common acquisition device cannot enable non-technical personnel to intuitively acquire the activity state and the fatigue degree of the measured muscle, professional technical personnel are required to process the electromyographic signals, the instantaneity and the effectiveness are lacked, and the cost is high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a detection system and a detection method for human body lumbar and back surface electromyographic signals, which can remove signal interference and output visual data to reflect muscle motion states and fatigue.
The purpose of the invention is realized by the following technical scheme:
a detection system for human body back surface electromyographic signals comprises: the acquisition unit and the signal processing terminal are connected in sequence; the acquisition unit is used for acquiring the electromyographic signals of the waist and back surfaces of the human body of the subject and sending the electromyographic signals to the signal processing terminal; the signal processing terminal comprises a digital filtering module, a signal rectifying module, an activity section detection module, a feature extraction module and a muscle fatigue degree analysis module and is used for processing the surface muscle electrical signals to form visual chart parameters; the digital filtering module is used for filtering the digital electromyographic signals; the signal rectification module is used for turning over all parts of the filtered electromyographic signals below a rest baseline to the baseline; the activity segment detection module is used for judging the starting time and the ending time of the muscle action by adopting a moving average method and determining the activity period as the action effective period of the electromyographic signal; the characteristic extraction module is used for extracting time domain characteristics of the corresponding electromyographic signals according to the action effective time period; the time domain features comprise an integral myoelectric value and a root mean square value; and the muscle fatigue analysis module is used for generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying and acquiring the muscle state and realizing the visualization of the acquired signal data.
Preferably, the expression of the integrated myoelectric value imeg and the root mean square value RMS is as follows:
wherein i is the amplitude measured by the discrete electromyographic signal at the corresponding sampling interval delta t, and N is the number of the collection points on the surface of the back of the human body of the subject.
Preferably, the collecting unit includes: the acquisition electrode, the amplifying circuit, the filter circuit, the analog-to-digital converter and the USB circuit are connected in sequence; the amplifying circuit comprises a main amplifier and a secondary amplifier; the filtering circuit comprises a high-pass filter, a low-pass filter and a trap circuit and is used for removing noise interference outside an effective frequency range of the electromyographic signal and removing power frequency interference of 50 Hz; an analog-to-digital converter for converting the analog electromyographic signal into a digital electromyographic signal; and the USB circuit is used for sending the digital electromyographic signals to the signal processing terminal. When the electromyographic signals of the surface of the back of the human body are detected, the collecting electrode is placed on the back of the human body, and the other end of the USB circuit is connected with the signal processing unit;
preferably, the collecting electrode is a fabric electrode and is attached to the flexible binding band, and magic tapes are arranged at two ends of the flexible binding band; the collecting electrode comprises three electrode plates, wherein two electrode plates are horizontally parallel to form differential amplification, and the third electrode plate is vertically arranged to serve as a reference electrode plate.
Preferably, the number of the collecting electrodes is 4, the number of the flexible binding bands is 2, two collecting electrodes are collected on one flexible binding band, and the 2 flexible binding bands are connected through the elastic band.
Preferably, the lumbar dorsal surface of the human body of the subject is the signal source muscle, and the L3 erector spinae and the T12 erector spinae are the signal source muscles.
A method for detecting electromyographic signals of the surface of the back of a human body comprises the following steps:
s1, collecting electromyographic signals of the waist and back surfaces of the human body of the subject;
s2, filtering the electromyographic signals, and turning over the parts of the filtered electromyographic signals below the rest base line to the base line;
s3, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
s4, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
and S5, generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying the muscle state, and realizing the visualization of the acquired signal data.
Preferably, steps S1 and S2 include: denoising and amplifying the electromyographic signals; removing noise interference outside an effective frequency range and 50Hz power frequency interference from the amplified electromyographic signals; the analog electromyographic signal is converted into a digital electromyographic signal.
Compared with the prior art, the invention has the following advantages:
the signal processing terminal comprises a muscle fatigue degree analysis module, wherein the muscle fatigue degree analysis module converts the extracted signal waveform and signal characteristic vector value (time domain characteristic value) of the activity section into a visual chart, and displays the visual chart on a computer interface, so that a non-professional can visually know the active state and the fatigue degree of the waist back muscle of a current subject through the chart, and can carry out comparative analysis through specific numerical values to master the current movement or operation intensity, and further, the waist back injuries of related high-intensity transport operators and sporters can be effectively avoided.
The invention carries out secondary filtering on the electromyographic signals. The digital filtering module of the signal processing terminal filters the collected electromyographic signals for the first time, removes noise interference outside an effective frequency band (20-500Hz) of the electromyographic signals and removes power frequency interference of 50 Hz; and filtering the signal for the second time by using a digital filtering module of the signal processing terminal to remove part of noise which cannot be filtered by a hardware circuit and improve the signal-to-noise ratio.
The collecting electrode is a fabric electrode and is attached to the flexible binding band. The fabric electrode has the advantages of large contact area and good conductivity, the position of the flexible binding band can be freely adjusted to be fixed on specific muscle collection of a human body, the magic tapes are attached to the two ends of the flexible binding band and can be adjusted according to waistline so as to be attached to the human body, the flexible binding band is connected with the elastic band, and the height of the flexible binding band can be freely adjusted.
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 schematic structural diagram of a system for detecting electromyographic signals of the back and waist surfaces of a human body.
Fig. 2 is a schematic block diagram of a signal processing terminal of the present invention.
Fig. 3 is a schematic structural diagram of the collecting electrode of the present invention.
Wherein, 1 is flexible bandage, 2 is the magic subsides, 3 is the electrode slice, and 4 are the elastic webbing.
Detailed Description
The invention is further illustrated by the following figures and examples.
The invention aims to provide a system and a method for detecting electromyographic signals of the surfaces of the waist and the back of a human body, which realize multi-channel acquisition of muscles of the waist and the back, achieve the aims of firm and comfortable electrode fixation, convenient acquisition, high signal-to-noise ratio, intuitive and understandable data graphs and low cost, and meet the requirements of non-professionals on detecting the state and the fatigue degree of the muscles of the waist and the back.
Referring to fig. 1-3, a system for detecting electromyographic signals of the surface of the back and the waist of a human body comprises: the acquisition unit and the signal processing terminal are connected in sequence; the acquisition unit is used for acquiring the electromyographic signals of the waist and back surfaces of the human body of the subject and sending the electromyographic signals to the signal processing terminal; the signal processing terminal comprises a digital filtering module, a signal rectifying module, an activity section detection module, a feature extraction module and a muscle fatigue degree analysis module and is used for processing the surface muscle electrical signals to form visual chart parameters; the digital filtering module is used for filtering the digital electromyographic signals; the signal rectification module is used for turning over all parts of the filtered electromyographic signals below a rest baseline to the baseline; the activity segment detection module is used for judging the starting time and the ending time of the muscle action by adopting a moving average method and determining the activity period as the action effective period of the electromyographic signal; the characteristic extraction module is used for extracting time domain characteristics of the corresponding electromyographic signals according to the action effective time period; the time domain features comprise an integral myoelectric value and a root mean square value; and the muscle fatigue analysis module is used for generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying and acquiring the muscle state and realizing the visualization of the acquired signal data.
It should be noted that the invention performs secondary filtering on the electromyographic signals. The first time is that the filter circuit of the acquisition unit filters the electromyographic signals, removes noise interference outside the effective frequency band of the electromyographic signals and removes 50Hz power frequency interference. And filtering the signal for the second time by a digital filtering module of the signal processing terminal. The digital filtering module is an FIR band-pass filter, the pass band range is 20-500Hz, and signal noise is further eliminated. This is because a part of the noise, which cannot be filtered out by the hardware circuit, is transmitted to the signal processing terminal along with the electromyogram signal. The digital filtering module is an FIR band-pass filter, the pass band range is 20-500Hz, and signal noise is further eliminated. The signal rectification module turns over all parts of the filtered electromyographic signals below the rest baseline to the baseline. The signal has the characteristic of zero mean value, positive and negative values are symmetrically distributed and have no practical significance, and the folded signal is convenient for subsequent signal processing and waveform observation. The active segment detection module effectively judges the starting and stopping time and the ending time of the current motion action and intercepts the segment of signals so as to facilitate the subsequent signal processing; the method comprises the steps of effectively judging the starting and stopping time and the ending time of the current motion action, and intercepting a signal to facilitate subsequent signal processing; the feature extraction module extracts the selected active segment signals as time domain feature vectors (RMS, imeg). Where RMS is the Root mean square value (Root mean square), iEMG is the Integrated myoelectric value (Integrated EMG):
in the present embodiment, the expressions of the integrated electromyography value imeg and the root mean square value RMS are as follows:
wherein i is the amplitude measured by the discrete electromyographic signal at the corresponding sampling interval delta t, and N is the number of the collection points on the surface of the back of the human body of the subject. The RMS reflects the energy consumed by the muscle of the signal source in the activity segment and directly reflects the average active state of the muscle in the exercise period; imeg represents the additive amount of the electromyographic value output over a period of time, reflecting the cumulative fatigue of the muscle over the exercise period. The activity and fatigue of the muscles of the lumbar and back are reflected by means of time-domain feature vectors (RMS, imeg) by 5-feature extraction of the signal processing system.
In this embodiment, the collecting unit includes: the acquisition electrode, the amplifying circuit, the filter circuit, the analog-to-digital converter and the USB circuit are connected in sequence; because the energy of the electromyographic signals is mainly distributed at low frequency of 20-500Hz and the amplitude change of the signals is 0.01 mu V-10mV, an amplification filter circuit is required to carry out noise reduction and amplification treatment on the electromyographic signals; the amplifying circuit comprises a main amplifier and a secondary amplifier, and the sum of the amplification times is about 1000 times; the filtering circuit comprises a high-pass filter, a low-pass filter and a trap circuit and is used for removing noise interference outside an effective frequency range of the electromyographic signal and removing power frequency interference of 50 Hz; the low-pass filtering process is used for processing noise above 500Hz, the high-pass filtering process is used for processing useless signals below 20Hz, and the notch processing process is used for removing power frequency interference of 50Hz and improving the signal-to-noise ratio.
In the present embodiment, an analog-to-digital converter for converting an analog electromyographic signal into a digital electromyographic signal; and the USB circuit is used for sending the digital electromyographic signals to the signal processing terminal. When the electromyographic signals of the surface of the waist and the back of the human body are detected, the collecting electrodes are placed on the waist and the back of the human body, and the other end of the USB circuit is connected with the signal processing unit.
The traditional surface electromyogram signal acquisition device generally adopts a surface electrode to be directly adhered to the surface of skin for signal acquisition, but the electrode is difficult to fix, the measurement process is easy to shift, the requirement on the test environment is higher, and meanwhile, a subject is difficult to carry out violent movement during measurement, so that long-term measurement cannot be realized. In the embodiment, the collecting electrode is a fabric electrode, the fabric electrode is fixed on the flexible binding band according to the muscle position of a testee, and magic tapes are arranged at two ends of the flexible binding band, so that the length can be adjusted according to different human body sizes for fixing and sticking; the collecting electrode comprises three electrode plates, wherein two electrode plates are horizontally parallel to form differential amplification, and the third electrode plate is vertically arranged to serve as a reference electrode plate. The number of the collecting electrodes is 4, so that the surface electromyographic signals of the muscles on the surface 4 of the back of the human body of the subject can be collected simultaneously. The distance is adjusted by the elastic belts, so that when the testees face different testees, the fabric electrodes can be guaranteed to accurately acquire corresponding myoelectric signals, the displacement is avoided in the movement process, and the device is suitable for acquiring the lumbar and back myoelectric signals of different people for a long time.
The quantity of electrode flexible bandage is 2, and two collection electrodes are gathered on a flexible bandage, connect through the elastic webbing between 2 flexible bandages.
In this embodiment, the lumbar dorsal surface of the subject is the source muscle, and the L3 erector spinae and T12 erector spinae are the source muscles.
The detection method of the human body back surface electromyographic signals, which is suitable for the detection system of the human body back surface electromyographic signals, comprises the following steps:
s1, collecting electromyographic signals of the waist and back surfaces of the human body of the subject; wherein, steps S1 and S2 include: denoising and amplifying the electromyographic signals; removing noise interference outside an effective frequency range and 50Hz power frequency interference from the amplified electromyographic signals; the analog electromyographic signal is converted into a digital electromyographic signal.
S2, filtering the electromyographic signals, and turning over the parts of the filtered electromyographic signals below the rest base line to the base line;
s3, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
s4, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
and S5, generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying the muscle state, and realizing the visualization of the acquired signal data.
The above-mentioned embodiments are preferred embodiments of the present invention, and the present invention is not limited thereto, and any other modifications or equivalent substitutions that do not depart from the technical spirit of the present invention are included in the scope of the present invention.
Claims (8)
1. A detection system for human body back surface electromyographic signals, comprising: the acquisition unit and the signal processing terminal are connected in sequence;
the acquisition unit is used for acquiring the electromyographic signals of the waist and back surfaces of the human body of the subject and sending the electromyographic signals to the signal processing terminal;
the signal processing terminal comprises a digital filtering module, a signal rectifying module, an activity section detection module, a feature extraction module and a muscle fatigue degree analysis module and is used for processing the surface muscle electrical signals to form visual chart parameters;
the digital filtering module is used for filtering the digital electromyographic signals;
the signal rectification module is used for turning over all parts of the filtered electromyographic signals below a rest baseline to the baseline;
the activity segment detection module is used for judging the starting time and the ending time of the muscle action by adopting a moving average method and determining the activity period as the action effective period of the electromyographic signal;
the characteristic extraction module is used for extracting time domain characteristics of the corresponding electromyographic signals according to the action effective time period; the time domain features comprise an integral myoelectric value and a root mean square value;
and the muscle fatigue analysis module is used for generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying and acquiring the muscle state and realizing the visualization of the acquired signal data.
2. The system for detecting electromyographic signals of the lumbar and back surfaces of a human body according to claim 1, wherein the expressions of the integrated electromyographic values imeg and the root mean square values RMS are as follows:
wherein i is the amplitude measured by the discrete electromyographic signal at the corresponding sampling interval delta t, and N is the number of the collection points on the surface of the back of the human body of the subject.
3. The system for detecting the electromyographic signals of the back and the waist of a human body according to claim 1, wherein the collecting unit comprises: the acquisition electrode, the amplifying circuit, the filter circuit, the analog-to-digital converter and the USB circuit are connected in sequence;
the amplifying circuit comprises a main amplifier and a secondary amplifier;
the filtering circuit comprises a high-pass filter, a low-pass filter and a trap circuit and is used for removing noise interference outside an effective frequency range of the electromyographic signal and removing power frequency interference of 50 Hz;
an analog-to-digital converter for converting the analog electromyographic signal into a digital electromyographic signal;
and the USB circuit is used for sending the digital electromyographic signals to the signal processing terminal.
When the electromyographic signals of the surface of the waist and the back of the human body are detected, the collecting electrodes are placed on the waist and the back of the human body, and the other end of the USB circuit is connected with the signal processing unit.
4. The system for detecting the electromyographic signals of the surface of the back and the waist of a human body according to claim 3, wherein the collecting electrode is a fabric electrode and is attached to the flexible binding band, and magic tapes are arranged at two ends of the flexible binding band;
the collecting electrode comprises three electrode plates, wherein two electrode plates are horizontally parallel to form differential amplification, and the third electrode plate is vertically arranged to serve as a reference electrode plate.
5. The system for detecting the electromyographic signals of the surfaces of the back and the waist of a human body according to claim 3, wherein the number of the collecting electrodes is 4, the number of the flexible binding bands is 2, two collecting electrodes are collected on one flexible binding band, and the 2 flexible binding bands are connected through an elastic band.
6. The system for detecting the electromyographic signals of the human lumbar and back surface of the subject according to claim 1, wherein the signal source muscles are the lumbar and back surface of the human subject, and the L3 erector spinae and the T12 erector spinae are used as the signal source muscles.
7. A method based on a human body back surface electromyographic signal detection system according to any one of claims 1-6, comprising:
s1, collecting electromyographic signals of the waist and back surfaces of the human body of the subject;
s2, filtering the electromyographic signals, and turning over the parts of the filtered electromyographic signals below the rest base line to the base line;
s3, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
s4, judging the starting time and the ending time of the muscle action by adopting a moving average method, and determining the starting time and the ending time as the action effective time period of the electromyographic signal;
and S5, generating a corresponding visual table and graph based on the time domain characteristic value, judging the fatigue of the muscle in the current motion state, displaying the muscle state, and realizing the visualization of the acquired signal data.
8. The method of claim 8, wherein between steps S1 and S2 comprises:
denoising and amplifying the electromyographic signals;
removing noise interference outside an effective frequency range and 50Hz power frequency interference from the amplified electromyographic signals;
the analog electromyographic signal is converted into a digital electromyographic signal.
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| CN117717343A (en) * | 2024-02-07 | 2024-03-19 | 深圳市前海蛇口自贸区医院(深圳市南山区蛇口人民医院) | Flexible surface myoelectric device |
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