Multilevel motion function evaluation method based on electromyographic signals

Abstract

The invention provides a multilevel motion function evaluation method based on electromyographic signals. The multilevel motion function evaluation method comprises the following steps of: S1, obtaining multi-channel EMG signals; S2, modeling based on a limb muscle multi-level dynamic feature analysis method to obtain multi-level dynamic features; S3, analyzing a muscle multi-level response mechanism and a motion function evaluation strategy according to the acquired multi-level dynamic features; S4, according to the modular structure and functional network features of the synergistic effect of the functional muscle groups, obtaining conditions aiming at limb movement mode abnormity and functional structure change; and S5, determining a limb muscle multi-level dynamic response mechanism and constructing a motion function evaluation strategy. According to the method, limb muscle multi-level dynamic response analysis is carried out from single-channel electromyographic signals, double-channel electromyographic signals and multi-channel electromyographic signals, a muscle multi-level dynamic response evolution rule is obtained, a comprehensive assessment strategy based on electromyographic multi-level dynamic characteristics is constructed, and a feasible basis is provided for effective assessment of the motion function state.

Description

technical field [0001] The invention relates to the field of signals, in particular to a multi-level motor function evaluation method based on electromyographic signals. Background technique [0002] With the in-depth discussion and research on the mechanism of the modern central nervous system, EMG signals have been continuously applied to health training, and EMG signals have been introduced into the field of control and function evaluation, and have been widely used to make reasonable muscle function evaluations. An important prerequisite for training instruction. In recent years, there have been many studies on muscle motor function, most of which focus on the research of EEG signal technology, using EEG signals for external devices, and analyzing and decoding through data classification. Enables users to understand brain activity and loss of function. However, it is not enough to understand all the movement information only through the EEG signal, so it is extended to...

AI Technical Summary

Problems solved by technology

However, only through the EEG signal is not enough to understand all the movement information, so it is extended to add the EMG signal, design virtual reality limb training equipment, and give corresponding guidance and suggestions by improving the evaluation system

[0003] At present, the research on the mechanism based on the analysis of the dynamic characteristics of single-channel, dual-channel and multi-channel EMG signals has been carried out successively, but the dynamic timing and complex scale and information at the contraction level There is still a lack of in-depth and effective analysis methods for the cross-frequency coupling and transfer direction of interaction, the modular structure of synergy, and the functional network.

The above studies also show that there are differences in the multi-level dynamic response characteristics in different stages of motion

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