How to use EMG for Digital Health?
It is used to measure and analyze the electromyographic signal emitted by muscle contraction, which represents the activity level of the muscle, and can be used to study muscle function. By measuring the data indicators of EMG response, such as RMS root mean square, iEMG integral, MF median frequency, etc., the individual’s nerve and muscle functional status can be further evaluated.
EMG signal analysis can be widely used in ergonomic analysis of muscle work, operation posture analysis, functional evaluation of rehabilitation status, fatigue identification, and research on prosthesis control and movement patterns.
The source of EMG signal
When the brain emits excitation and conducts downward, the cell bodies and dendrites of motor neurons in the central nervous system generate electrical impulses under the stimulation from synapses, which are conducted along the axons of neurons to the junctions of nerves and muscles at the terminals.
When a motor nerve touches a muscle, its axons branch off to many muscle fibers, each of which terminates on a muscle fiber to form a synapse called a motor endplate, and the motor potentials conducted to the axon terminals cause the nerve-muscle junction to release the chemical acetylcholine , Acetylcholine changes the ion permeability of the motor endplate to generate endplate potential.
This endplate potential in turn makes the muscle cell membrane reach the depolarization threshold potential, which generates the motor potential of the muscle fiber and propagates in both directions along the muscle fiber. It causes a series of changes in the muscle fiber, which produces the contraction of the muscle fiber. The contraction of numerous muscle fibers produces muscle force.
It can be seen that the propagation of the motor potential of the muscle fibers leads to muscle contraction, and the propagating electrical signal causes a current field in the soft tissue of the human body, and a potential difference appears between the detection electrodes, that is, an electromyographic signal.
Measurement method of signal
EMG signals are divided into two main ways: surface muscle signals and needle electrode signals.
Surface electromyography
sEMG signals are bioelectrical currents generated by contraction of muscles on the surface of the human body. The nervous system controls muscle activity (contraction or relaxation), and different muscle fiber motor units on the surface of the skin generate different signals at the same time.
Surface EMG is actually a combination of time and space signals composed of electrophysiological signals of a single motor unit, which reflects the electrophysiological properties of the entire muscle (all muscle fibers involved in contraction). At the same time, it is a safe, easy to master, non-invasive method for recording, which can objectively quantify muscle energy. It is a commonly used method of signal acquisition.
Features:
- One-dimensional temporal action potential sequences.
- The amplitude of the AC signal is generally proportional to the strength of the muscle movement.
- Generally, it is generated 30-150ms ahead of the limb movement, and the movement can be judged in advance.
- A non-stationary micro-electric signal whose amplitude is 0-1,5mv, the useful signal frequency is 0-500HZ, and the main energy is concentrated in 20-150HZ.
Needle Electrode Electromyography
The needle electrode signal is the sum of the motor unit potentials in a limited range around the needle electrode collected by the needle electrode inserted into the muscle. The needle electrode signal has the advantages of less interference and easy identification; the needle electrode signal can further explore the bioelectric signal characteristics of a single motor unit that controls muscle activity, including the motor unit activity based on the recruitment timing, and the discharge timing of a single motor unit.
The one-by-one display, the changing characteristics of motor unit recruitment and de-recruitment states, as well as the time-varying discharge rate (pulse times/s) of a single motor unit, muscle force contraction curves, etc. Therefore, compared with conventional sEMG, dEMG can more deeply explore the details of muscle contraction activities, can more clearly reflect the discharge characteristics of a certain motor unit.
And more deeply explore the characteristics of the functional level of cells than sEMG, but The collection process will cause harm to the human body. Not suitable for scientific research.
The functional unit of muscle contraction is a motor unit, which consists of a single alpha motor neuron and all the muscle fibers it activates.
EMG signal analysis in ErgoLAB
The analysis of the EMG signal mainly includes the original surface signal analysis and the processed data analysis. The data analysis mainly focuses on the time domain and frequency domain analysis. The purpose of signal analysis is mainly to study the correlation between the time and frequency characteristics of surface signals and muscle structure, muscle activity state and functional state, to explore the possible causes of surface signal changes, and then to effectively use signal changes to reflect muscle activity. and function.
As the most direct manifestation of the occurrence of activity and the state of rest, the original surface signal can be analyzed without considering the amplitude, that is, the initial relationship of the signal during muscle activity. Intensity and height can reflect the amplitude and strength of muscle contraction to a certain extent.
The higher the density and height, the stronger the surface signal and the stronger the contraction; the data analysis after processing is to directly record the original surface signal, and use the signal processing system built in the software to analyze the original signal.
Perform rectification, smoothing, and MVC normalization, and further calculate and analyze. ErgoLAB provides raw surface signal analysis as well as processed data analysis methods such as time domain analysis, frequency domain analysis and segmental analysis.
Time domain analysis
Time domain analysis is to treat EMG signals as a function of time, and to perform some statistical analysis with time as an independent variable, without involving any non-time independent variables. Time domain analysis can provide you with indicators to evaluate the change characteristics of curves in the time dimension, including:
- Mean (μV) average value of EMG, indicating the average level of signal
- Max (μV) maximum discharge capacity of muscle activity
- Min (μV) Minimum discharge capacity of muscle activity
- Variance (μV) variance, reflecting the trend of the degree of dispersion of the signal
- iEMG (μV) Integral EMG refers to the sum of the area enclosed by the curve in unit time after the measured surface signal is rectified and smoothed. It represents the total discharge of motor units when muscles participate in activities within a certain period of time, reflecting a period of time. The EMG activity of the inner muscles is strong or weak. The high and low values
of iEMG respond to the size of the discharge of each motor unit and the number of muscle fibers involved in muscle contraction during exercise. Generally, the larger the amplitude, the heavier the fatigue. It is an important indicator for evaluating muscle fatigue. - Mean Absolute Value (μV) Mean Absolute Value
- Standard Deviation (μV) standard deviation, reflecting the trend of the degree of dispersion of signals
- Range (rmp) EMG amplitude RMS (μV) root mean square, refers to the root mean square value of all amplitudes within a certain period of time, and describes the average change characteristics of surface EMG in a period of time.
- The magnitude of the RMS is determined by the amplitude of the surface EMG. You can determine the time and degree of fatigue by comparing the RMS in different periods. Generally speaking, whether it is static or dynamic motion, the amplitude of the surface EMG signal will increase from the initial state to the fatigue state, that is, with the increase of fatigue, the RMS will increase.
Frequency domain analysis
Frequency domain analysis is the analysis of the frequency characteristics of biological real-time signals, also known as power spectrum analysis. The frequency domain signal is obtained by the time domain signal through the fast Fourier transform (FFT), which can reflect the strength of the EMG signal in different frequency ranges, and obtain the information about the frequency characteristics of the EMG signal. Frequency domain analysis metrics include:
- Median Frequency (Hz) Median Frequency (MF): refers to the middle value of the discharge frequency, that is, the middle value of the discharge frequency during muscle contraction, which generally decreases with the increase of the exercise time period. Due to the different composition ratio of fast and slow muscle fibers in skeletal muscle, the MF values
of different parts of skeletal muscle are different. Fast-twitch fibers are excited at high frequencies, while slow-twitch fibers are at low frequencies. - Mean Power Frequency (Hz) Mean Power Frequency (MPF): refers to the average value of the frequency within this period. In the state of muscle fatigue, the surface EMG frequency domain index MPF showed a decreasing change.
Generally, during moderate to high-intensity exercise, MPF and MF values
Periodic Analysis
Dynamic periodic force analysis can automatically identify time segments of intermittent force exertion of tasks, and perform segmental overlay statistics. It is more suitable for long-term repetitive human operation tasks. The periodic analysis indicators mainly include:
- Start Time Dynamic force cycle start time
- End Time Dynamic force cycle end time
- RMS dynamic force root mean square
- Mean Absolute Value (μV) Mean absolute value of dynamic force
EMG Application
Surface EMG signals can well reflect the movement intention of the human body and directly reflect the degree of muscle fatigue, and have the advantages of non-invasive measurement, convenient acquisition, and relatively small disturbance noise. Human-computer interaction, etc.
- Nerve conduction velocity was measured using electromyography. By administering electrical stimulation at two or more points of the nerve pathway, the muscle evoked potentials innervated from the nerve are measured.
- The dynamic response to muscle fatigue was assessed using surface electromyography. In the process of muscle isometric contraction to fatigue, the amplitude of EMG increases with the deepening of fatigue, that is, the amplitude of integral EMG and root mean square increases.
- Measure the relationship between muscle strength and EMG. When the muscle contracts with different loads, the EMG signal iEMG is proportional to the muscle strength, that is, the greater the muscle tension, the greater the iEMG.
- Measuring the state of electromyography under concentric and eccentric muscle movements
- Assess the relationship between muscle fiber type and muscle strength, muscle fatigue, and electromyography
- Research on multi-mode human-machine interface based on EMG signal. Different action poses have different EMG signal characteristics. Through the detection, processing and feature value extraction of EMG signals, the recognition results are used as control information for people and the environment.
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