Multi-Source Integration based Transfer Learning Method for Cross-User sEMG Gesture Recognition

Abstract

Surface electromyography (sEMG) is a kind of bioelectric signal of the human body, containing a wealth of action intentions. Among various gesture recognition solutions, sEMG-based solutions show irreplaceable advantages by directly sensing and parsing human muscle activities and converting them into interactive commands. However, sEMG is sensitive to many factors related to users, and there are individual differences among different users. The gesture recognition model trained based on the data of existing users has poor recognition accuracy on the data of new users directly. Excitingly, transfer learning breaks through the independently identical distribution (I.I.D.) assumption of data in different domains, so it shows great potential for cross-user sEMG gesture recognition. Therefore, we propose a Multi-Source Integration based Transfer Learning (MSITL) method to explore cross-user gesture recognition in this paper. MSITL is composed of two main parts, the Source Model Construction Strategy (SMCS) and the Target Model Adaptation Strategy (TMAS). SMCS is a layered integration model. The first layer builds a model for each user. The second layer integrates multiple models through simple majority voting. TMAS is mainly divided into three steps. The first step is to use the target domain data to evaluate the source domain model and obtain the evaluation score of the individual classifier; The second step is to fine-tune the individual classifiers under the guidance of the evaluation scores; The third step is to integrate the adjusted model. Detailed experiments are conducted on benchmark sEMG gesture recognition datasets, including NinaPro (i.e., DB1) and CapgMyo (i.e., DB-a, DB-b, and DB-c). The proposed method achieves significant improvements in performance compared with current state-of-the-art methods.