Muscle Fatigue Detection And Human-Machine Interface Performance Optimization Based On Multi-Source Signal Acquisition System

The collection and analysis of muscle contraction information has always been a research area of great significance.Using the relevant information of muscle contraction,we can analyze the muscle state and movement intention to further understand the principle of muscle movement and control external equipment.Muscle fatigue is a common phenomenon,which has a negative impact on work efficiency and quality of life,and also affects the decoding accuracy of the human-machine interface.It is of great value to study muscle fatigue.However,the related information of muscle contraction is multi-dimensional,and there are many kinds of signals that can be collected.In order to fully understand the mechanism of muscle fatigue and improve the performance of the human-machine interface in fatigue state,it is necessary to collect and analyze a variety of muscle signals.The main research work of this paper is to design and develop a multisource signal acquisition system which can collect surface electromyography(s EMG),mechanomyography(MMG)and near-infrared spectroscopy(NIRS)at the same time.The system is used to detect muscle fatigue and improve the performance of human-machine interface in fatigue state.The main research content is divided into the following parts.Firstly,a multi-source signal sensing system is developed,and the system is optimized from the aspects of structure layout,size and electrical performance.What's more,the stability and rationality of the sensor system are evaluated.For stability,performance indicators measured by the experiment is used to compare with the standard.For rationality,the blood oxygen concentration curve measured by the sensor system is compared with that measured by a reliable blood oxygen detection device.Grip strength increasing experiment is carried out to prove that the data measured by the system accords with the objective law of muscle contraction.Furthermore,a reasonable experimental paradigm is designed to induce muscle fatigue under three force levels.The experimental results show that there is a relationship between anaerobic metabolism and the decrease of s EMG/MMG median frequency.Last but not the least,the pattern recognition experiments of four kinds of hand gestures are designed and carried out.The statistical model of the relationship between signals and motions is established.The comparison of classification accuracy proves that forearm muscle fatigue can lead to the decrease of classification accuracy,and the acquisition and decoding of these three muscle signals can better overcome the adverse effect of fatigue on classification accuracy.