Research On Mapping Mechanism Of Surface Electromyography Feature And Human Motion Function Status

In the motion-assisted process of wearable robots,it is beneficial to realize the master/slave motion control of wearable robots by acquiring human motion information and effectively recognizing human body motion function,which provides a new research idea for improving the flexibility of motion assist.Since the surface electromyography(sEMG)is the human body's own resources,it contains the information of the muscle state and function of the human body.By studying the sEMG decoding technology,it is expected to explore the evaluation index of the human body function state.So that the robot control system provides the ideal master/slave flexibility aid based on the human body motion function.Aiming at the problem of human motor function recognition,in this paper,a multi-dimensional evaluation index of motor function based on fatigue,muscle strength and muscle contraction and relaxation ability is proposed.The Mapping Law of fatigue eigenvalue,muscle strength,fractal dimension characteristics and motor performance state is emphatically studied.The main research contents are as follows:Firstly,the research history and development trend of sEMG at home and abroad are summarized,and the processing technology and research methods of sEMG are explored in depth.Secondly,the experiment of human lower limb movement is designed to complete the signals acquisition.Based on the characteristics and generating principle of sEMG,the collected signals are preprocessed.The collected signals are processed by median filtering and band-pass filtering to obtain the filtered signals.The validity of the filtering method is proved by comparing the filtered and pre-filtered signals.Thirdly,the evaluation of human exercise fatigue based on sEMG;In the first step,the causes of fatigue during exercise and the characteristics of time domain and frequency domain variation of sEMG during fatigue process are discussed in depth.The second step is to extract the time domain index and frequency domain index for evaluating human exercise fatigue.Finally,the obtained indicators are processed to obtain the trend of fatigue changes during human exercise.Fourth,calculating the muscle strength of the human body during exercise;In the first step,the current sEMG-based muscle force calculation method is analyzed and studied.The second step is to select the appropriate muscle strength calculation method.The muscle strength calculation method based on Hill three-element model is adopted in this paper.The muscle activity,force-length curve and force-speed curve establish a muscle model.finally,the muscle strength is obtained by the muscle model,and the calculated actual muscle strength and theoretical muscle strength are compared to verify the feasibility of the experimental method.Fifth,comparing the muscle contraction relaxation ability of different individuals in the same exercise by nonlinear analysis of sEMG.In the first step,the phase space reconstruction and the calculation of the maximum Lyapuf exponent can be used to determine the sEMG is the chaotic signal characteristic between the random signal and the periodic signal.The second step is to analyze the signal complexity based on the nonlinear feature.The complexity of the signal is obtained by calculating the fractal dimension,and the complexity of the sEMG signal under different active states of the muscle is analyzed and compared.A new indicator is proposed to evaluate the ability of muscles to contract and relax in non-specific individuals.Sixth,the distribution of different subjects in the multidimensional evaluation index space is studied and the mapping relationship between sEMG signal characteristics and human motion function is comprehensively explored based on the theory and data.