The invention relates to a myoelectric
gesture recognition method based on an RNN-CNN architecture. The method comprises the following steps of performing
feature extraction on each channel
signal byusing an RNN architecture according to a
time sequence characteristic of a
myoelectric signal, and further extracting a fused feature map by using a CNN architecture, and mainly comprises the following steps of preprocessing the data, using an RNN module to perform preliminary
feature extraction on the preprocessed data, using a fusion module to perform fusion
processing on an output result of theRNN; using a CNN module to perform
feature extraction and analysis on an output result of the fusion module; and using a classification module to judge the input gesture
signal by the model output, namely judging which gesture type the electromyographic
signal belongs to according to the currently input electromyographic signal. According to the method, the
time sequence relevance and characteristics of the data can be effectively extracted, and meanwhile, the
gesture recognition rate is improved; an extreme value point selection and splicing method is introduced at a data preprocessing stage, so that the model
training time is reduced, and the mutual interference between the channels is avoided; finally, at the fusion stage, the relevance of the multiple channels is utilized, so that theidentification of the electromyographic signals is facilitated.