Muscle Fatigue Classification Research Based On JAB-SVM

At this stage,the economic level of society has been significantly improved,and people's health awareness is also constantly strengthening.Muscle system is an important part of human organs,which provides power for every life movement.However,when the muscle works continuously,its working ability will decrease obviously,and muscle fatigue will occur,which not only affects the normal activities of the human body,but also easily leads to muscle injury.Therefore,the alleviation and treatment of muscle fatigue is a problem worthy of attention.Recognition and detection of muscle fatigue is the key of this study,which is of great significance to kinematics and medical research.Muscle fatigue is a dynamic and continuous process,its state is mainly divided into two stages: fatigue stage and non-fatigue stage.There are many methods to identify and classify muscle fatigue.In this paper,Jackknife-After-Bootstrap(JAB)is used to classify the muscle fatigue feature information in support vector machine(SVM)model.The feature extraction and data processing of muscle fatigue data are described.The knowledge of Jackknife method,Bootstrap method,JAB and SVM are also described in detail.The effectiveness of JAB-SVM and SVM models in muscle fatigue classification is compared.The experimental results show that the introduction of JAB method in SVM classification model not only improves the accuracy of muscle fatigue classification model,but also improves the stability of classification model,which proves the feasibility and superiority of JAB method.