Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm

Gait phases are widely used in exoskeleton movement control. Surface electromyography (sEMG) is predictive and plays an important role in gait phase recognition. The purpose of this study is to improve the stability and accuracy of gait recognition methods based on the sEMG signals of lower limbs. First, we presented a LDA-PSO-LSTM algorithm based on feature combination selection and verified its recognition accuracy through experi-ments. LDA-PSO-LSTM had an average recognition rate of 94.89% and a maximum accuracy of 97.02%. Second, we tested and compared the recognition accuracy of LDA-LSTM (92.17%). Experiments showed that the PSO optimization model had good recognition performance. Finally, we compared LDA-LSTM with all classifier combinations and concluded that the LDA-LSTM method has the highest recognition rate among a series of method combinations. The results indicated that LDA-PSO-LSTM as a classification model has apparent advan-tages in gait recognition. LDA-PSO-LSTM provides more accurate gait phase results for lower limb exoskeleton control. This method is beneficial to the development of the exoskeleton gait recognition system.

Automated summary

This study proposes a gait recognition method based on sEMG signals, which has been proven to have high stability and accuracy through experiments. This method is of great significance for lower limb exoskeleton control, and brings benefits to the development of exoskeleton gait recognition system.