A method for detecting the starting point of muscle activation of high-density or multi-channel EMG signals

Abstract

The invention relates to a method for detecting the starting point of muscle activation of high-density or multi-channel myoelectric signals. First, the activation threshold of each channel is determined based on statistical information; Assign values ​​to the muscle activation starting points of the remaining channels within a time window; then divide the starting points of each channel into three categories through a clustering algorithm; finally exclude the influence of local muscle misoperation and external interference, and determine whether the muscle is activated and the corresponding activation starting point. The present invention accurately and real-time judges whether the muscle is activated and the corresponding activation starting point from the high-density or multi-channel electromyographic signal by utilizing the correlation among channels in the high-density or multi-channel electromyographic signal.

Description

technical field [0001] The invention relates to the field of physiological electrical signal detection, in particular to a method for detecting the starting point of muscle activation of high-density or multi-channel electromyographic signals. Background technique [0002] The joints of the human body rely on muscle contraction to ensure limb behavior. The brain issues instructions, and the muscles respond through nerve conduction, changing from a resting state to an active state, and electromyographic signals can be detected through electrodes. EMG signals are derived from the action potentials of the nerves that control muscle contraction, which reflect the subjective movement intention of the human body as well as the activation and contraction characteristics of the muscles, and are widely used in the fields of intelligent rehabilitation training equipment and rehabilitation assessment. [0003] Human joint activities are completed by the cooperation of multiple muscles....

AI Technical Summary

Problems solved by technology

These two high-density or multi-channel muscle activation detection methods are easy to make misjudgments on local muscle malfunctions and external interference, with low accuracy and poor real-time performance.

However, there is a correlation between the channels of the high-density or multi-channel EMG signal, and these two methods do not consider the correlation between the channels in the EMG signal.

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