Improving the Robustness and Adaptability of sEMG-Based Pattern Recognition Using Deep Domain Adaptation

The pattern recognition (PR) based on surface electromyography (sEMG) could improve the quality of daily life of amputees. However, the lack of robustness and adaptability hinders its practical application. To realize the long-term reliability and user adaptability simultaneously, a novel multi-task dual-stream supervised domain adaptation (MDSDA) network based on convolutional neural network (CNN) was proposed. A long-term multi-subject sEMG signal acquisition was conducted to validate the performance of MDSDA, recruiting 12 able-bodied subjects. A total of thirty gestures were used for the acquisition, including one set of static gestures and two sets of dynamic gestures. The long-term multi-subject sEMG dataset is publicly available at the website. Four train-test estimations were designed to evaluate the robustness and adaptability of MDSDA. The results showed that MDSDA outperformed CNN and fune-tuning. Furthermore, we studied the divisibility between static and dynamic gestures that performed similar actions. The outcomes demonstrated that there existed high separability between them. This may be helpful to reduce the signal collection burden. Experimental results proved MDSDA has the potential to provide a robust and generalized PR system for the clinic applications.