A Gesture Recognition Method Based on MIC-Attention-LSTM

다국어 초록 (Multilingual Abstract)

A gesture recognition method based on the maximal information coefficient attention-based long short-term memory (MIC-Attention-LSTM) algorithm was proposed to increase the accuracy of gesture recognition using high-density surface electromyography (HD-sEMG) and decrease the redundancy between HD-sEMG. The correlation number was used to reduce 10 time-domain features first, and then five features were chosen to create the best feature set. Next, MIC was employed to establish various reduction thresholds, divide various channel combinations, and determine the correlation between various signal channels. The best channel combination was chosen based on the classification accuracy of the final model, which was created by LSTM and Attention-LSTM. The classification results showed that the LSTM classification model achieved the highest classification accuracy of 87.27% and 89.91%, respectively, which were 1.41% and 1.71% higher than that without channel reduction, demonstrating the efficiency of the channel reduction method. Compared to the LSTM model, the classification accuracy of the Attention-LSTM model was 9.47% better after the feature and channel reduction of the sEMG was complete. This finding showed that the Attention mechanism algorithm could efficiently highlight the weight of key signal sequences and enhance the classification accuracy of LSTM.