The Assessment Study Of Electrically Evoked Muscle Fatigue Based On Myoelectric Signals

Along with the economic development and investment on health, electronic devices for household health care, such as electrical stimulators become more and more popular. However, fatigue caused by long term use of such instruments hampers the potential applications. In this article, the progress of fatigue during and after electrical stimulation is investigated, and a real-time measure method of muscle fatigue based on surface electromyography is established, which is the foundation of muscle fatigue conditioning.The subjects are divided into two groups:1) electrical stimulation fatigue and2) voluntary movement fatigue. Maximum grip strength which is the most accurate indicator in clinical diagnosis for muscle fatigue is adopted to assess the capacity change in fatigue models after electrical stimulation (the stimulation to cause fatigue, with frequency of5Hz and time course of5min) or voluntary movement. Electrical stimulus (with frequency of1Hz and time course of5seconds to induce myoelectric signal) is applied after the occurrence of fatigue both in the groups of electrical stimulation and voluntary movement, and the induced M-Wave signal at flexor carpiulnaris in the right forearm and the resting signal after that are measured. The pre-conditioning is applied to induce myoelectric signal and resting myoelectric signal. Finally, the power spectrum and nonlinear power spectrum entropy are used for feature extraction, and approximate entropy is used for statistical analysis.The result shows that there is significant difference between the fatigue processes of the electrically evoked pattern and the voluntary pattern. The average maximum grip strength (MGS) has a monotonic decrease in the subjects of the voluntary fatigue pattern, whereas in the initial phase of electrically evoked fatigue experiment, the average MGS has a tendency of increase, and then a decrease occurs which indicates fatigue in muscles, and this phenomenon demonstrates that the electrical stimulation plays a role of increasing the maximum working capacity. The analytical results of electrically evoked fatigue based on myoelectric signal show that when fatigue occurs, the low-frequency component of the evoked myoelectric signal will increase in accompany with the decrease of the high-frequency component which means a left-shift in the power spectrum of the signal. In the first phase of electrically evoked fatigue experiment, the power spectrum entropy and the approximate entropy of the evoked myoelectric signal have a tendency of increase, while along with the degree of fatigue increases; the power spectrum energy increases in resting myoelectric signal with a little decrease in medium-frequency, and that the left shift is not obvious in power spectrum, as well as the first step of electrically evoked fatigue experiment, the power spectrum entropy of resting myoelectric signals presents an increasing tendency with a relative decrease afterwards, but the entropy of the entire course remains bigger than the initial value except for a period of time at the end, and the approximate entropy of resting myoelectric signals show an downward tendency in the first phase of experiment. The entropy decrease, which indicates that in the first phase, the excitability of neuromuscular fibers is promoted, that the complex of the myoelectric signal increases which results to a more lively response of the muscle fibers. The analytical results of the voluntary fatigue experiment based on myoelectric signal show that when fatigue occurs, the power spectrum of the evoked myoelectric signal has a left-shift and the low-frequency component will increase in accompany with the decrease of the high-frequency component, in addition the power spectrum entropy and the approximate entropy of the evoked myoelectric signal decreases monotonically. The results of resting myoelectric signal show that the power spectrum energy increases with a little decrease in medium-frequency. In the entire experiment of the voluntary fatigue experiment, there is a little difference between two results of the power spectrum entropy in adjacent resting myoelectric signals, the same as the approximate entropy. When fatigue occurs, the results of entropy don’t show a significantly reduced change. The analysis of two kinds of muscle fatigue based on the evoked myoelectric signal show that the power spectrum gets a same result with the being agreed conclusion, and the analytical result of power spectrum entropy and approximate entropy is consistent with the results of the MGS. However, the changes of characteristics extracted from the resting myoelectric signal don’t agree with the results of MGS. Thus, the characteristics of the electrically evoked signal show the change of fatigue in muscles better than the one of resting signal. In comparison with all the thresholds change in the course of muscle fatigue, it is shown that electrical stimulation can be used to increase the excitability and the contraction capacity of the muscles in the effective period. And in this context, the effective period is about25minutes.This research validate that the electrical stimulation on person can be divided into two phases:In the first phase, the electrical stimulation increases the excitability of the muscle and enhances the contracting ability of the muscle. In the second phase, the electrical stimulation will result in the fatigue in muscles. The Power spectrum entropy and approximate entropy of M signal in electrically evoked EMG can be used to detect the fatigue process exactly, in contrast to the resting EMG. The proposed quantity analyzing method based on the surface EMG in this dissertation sets the foundation for the study of the mechanism of the fatigue evolvement, fatigue supervision and regulation.The novelties of this study are:1. Resting EMG can be adopted as a measure to estimate the fatigue in muscles in the experimental study.2. A new processing method to extract approximate entropy of M signal of Evoked EMG is proposed.3. The fatigue in muscles can be divided into two phases, and a quantitative measure is proposed.