The invention provides a gesture recognition method based on multichannel electromyographic signal correlation. The gesture recognition method comprises the following steps: firstly, de-noising electromyographic signals acquired by each channel; detecting a movable section according to the signal amplitude intensity; then, performing structured processing on the active section signal; processing the signal into a format with time correlation by superposing a plurality of continuous time windows; and finally, realizing a hybrid neural network CRNet based on the CNN + RNN neural network, and establishing a classifier for gesture recognition, wherein the input of the classifier is a signal subjected to structured processing, and the output of the classifier is a gesture classification probability, and the trained classifier is utilized to perform gesture recognition. For the gesture recognition method, only a plurality of myoelectricity sensors are used for collecting original signals while extra complex equipment is not needed, so that operation is convenient, and environmental adaptability is good. According to the gesture recognition method, the noise in the signal can be effectively removed, and the used classifier reduces the computing resources and improves the recognition efficiency, and the gesture recognition method is more suitable for engineering application.
Gesture recognition method based on multichannel electromyographic signal correlation