Analysis And Rehabilitation Of Hand Muscle Synergy

Stroke is an acute cerebrovascular disease that can cause limb disorders,especially hand motor dysfunction,and lead to difficulty in taking care of activities of daily life(ADL).Therefore,studying the mechanism of hand motor rehabilitation is crucial for improving brain nerve function and enhancing daily activity ability.At present,hand movement rehabilitation strategies are mainly divided into traditional rehabilitation therapy,external mechanical assisted limb training,drug and minimally invasive treatment,physical factor therapy,and so on.The purpose of rehabilitation therapy is to restore and improve the patient’s ADL and achieve normal motor behavior.All human motion behaviors are inseparable from the control mechanism of the Central Nervous System(CNS),and the redundant control problem of muscle coordination for human motion provides a reliable basis for explaining the control mechanism of CNS.Muscle coordination is the modularization of motion control,simplifying the hierarchical control of the motion system,and thus revealing the internal functional patterns of the nervous system.Therefore,this study focuses on the coordination and rehabilitation of hand movement muscles.Based on the analysis of muscle coordination,FES rehabilitation strategies are explored to explore muscle coordination issues caused by CNS injuries,providing theoretical support for hand movement rehabilitation strategies.Firstly,analyze the types,characteristics,indicators,and applications of electromyography signals,and select surface Electromyography(s EMG)as a research tool for muscle synergy based on the generation mechanism and usage of electromyography signals.At the same time,summarize the current status of muscle synergy analysis methods based on non negative matrix decomposition(NMF),and analyze the application characteristics and limitations of different methods.Secondly,for hand movement disorders,experimental paradigms were designed for non electrical stimulation and electrical stimulation of wrist flexion and extension movements,and corresponding surface electromyographic signals were collected and preprocessed.NMF was used to conduct muscle synergy analysis for healthy subjects who used functional electrical stimulation(FES)as a means of rehabilitation before and after the experiment,Propose a multi-scale non parametric Bayesian layered Dirichlet improved non negative tensor model framework(DPNTF)for muscle synergy analysis.Analyze the experimental data of healthy young subjects before and after electrical stimulation rehabilitation.The experimental results show that DPNTF can more effectively extract hidden features and frequency domain collaborative features from s EMG than NMF,And the frequency band of the spectral components before and after electrical stimulation treatment is widened.Finally,the method studied was applied to the muscle synergy analysis of hand surface electromyography signals in hand dysfunction subjects.By comparing and analyzing the muscle synergy before and after FES rehabilitation,a deep understanding of the multi domain characteristics of human muscle synergy was provided,providing a theoretical reference for FES as a personalized treatment strategy for stroke patients in clinical exercise rehabilitation.