Dual layer transfer learning for sEMG-based user-independent gesture recognition

Abstract

During the last few years, significant attention has been paid to surface electromyographic (sEMG) signal–based gesture recognition. Nevertheless, sEMG signal is sensitive to various user-dependent factors, like skin impedance and muscle strength, which causes the existing gesture recognition models not suitable for new users and huge precision dropping. Therefore, we propose a dual layer transfer learning framework, named dualTL, to realize user-independent gesture recognition based on sEMG signal. DualTL is composed of two layers. The first layer of dualTL leverages the correlations of sEMG signal among different users to label partial gestures with high confidence from new users. Then, according to the consistencies of sEMG signal from the same users, the rest gestures are labeled in the second layer. We compare our method with three universal machine learning methods, seven representative transfer learning methods, and two deep learning–based sEMG gesture recognition methods. Experimental results show that the average recognition accuracy of dualTL is 80.17%. Comparing with SMO, KNN, RF, PCA, TCA, STL, and CWT, the performance improves 24.26% approximately.