Contrastive Domain Adaptation: A Self-Supervised Learning Framework for sEMG-Based Gesture Recognition

Gesture recognition using surface electromyography (sEMG) shows its great potential in the field of humancomputer interaction (HCI). Previous works achieve relatively good performance based on the assumption of invariant statistic distribution. However, the practical application effect is unsatisfactory due to the problem of domain shift. Existing approaches need plenty of labeled sEMG samples from target scenarios for calibration, which is burdensome for experimenters and users. In this work, we present a contrastive self-supervised learning framework (ConSSL) for sEMG-based gesture recognition to realize domain adaptation in target domains. After pretraining on a bunch of unlabeled samples, only a small number of labeled samples are needed for calibration and domain adaptation. Experimental results indicate that the proposed framework outperforms other approaches even if = 50% labeled samples in target scenarios are available and achieves the state-ofthe-art.