Muscle fatigue advanced prediction and classification method based on surface electromyographic signals

Abstract

The invention relates to a muscle fatigue advanced prediction and classification method based on surface electromyographic signals. The method comprises the following steps of: (1) selecting the surface electromyographic signals of muscles related to joint movement; (2) preprocessing the acquired surface electromyographic signals, then carrying out segment handling on the signals, and extracting nonlinear characteristic parameters from each segment of signals to serve as fatigue characteristic vectors, the nonlinear characteristic parameters including a wavelet packet entropy, LZ complexity and a multi-scale entropy; (3) respectively carrying out characteristic parameter prediction on each characteristic parameter by adopting an improved adaptive normalized least mean square filter (NLMS)algorithm, and by the improved adaptive NLMS algorithm, performing advanced prediction on the characteristic parameters according to a set advanced prediction time period through utilizing the characteristic of adaptive update of an NLMS in each time step; and (4) carrying out fatigue classification identification on the predicted characteristic parameters by adopting an improved cerebellum modelneural network. The method can predict muscle fatigue in advance.

Description

technical field [0001] The invention belongs to the technical field of surface muscle signal classification, and in particular relates to a method for predicting and classifying muscle fatigue in advance based on surface myoelectric signals. Background technique [0002] In daily life, muscle fatigue is a common physiological phenomenon, usually due to the feeling of muscle weakness or soreness caused by long-term exercise or exertion. Muscle fatigue is defined as the inability of the body to maintain the expected exercise intensity due to a temporary decrease in the contractility of the muscular motor system. The assessment of muscle fatigue has a wide range of applications in many fields. For example, in the fields of rehabilitation medicine and sports kinematics, muscle fatigue can be used to assess the intensity of patient exercise or athlete training, and to formulate individual exercise or Training programs, preventing permanent muscle damage from overexertion and mor...

AI Technical Summary

Problems solved by technology

But muscle fatigue is a complex physiological state that is difficult to assess and quantify because it reflects both physical and psychological aspects

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