Deep Feature Learning From Electromyographic Signals for Gesture Recognition Systems

Deep learning applied to electromyography (EMG) signals enables accurate hand gesture recognition, revolutionizing diverse applications such as human-machine interaction, neural interfaces, and rehabilitative robotics. A well-designed deep learning architecture is crucial for accurately and robustly modeling and decoding the multidimensional information embedded in the EMG data. This survey presents a comprehensive review of state-of-the-art deep learning models and, for the first time, offers a categorization of advanced architectures from the perspective of data representations. EMG, as a distinctive biosignal modality, can be characterized through multiple representational forms, including temporal waveforms, spatial images, spectral domains, and graph-based structures comprising interconnected nodes. Consequently, the optimal model architecture is closely tied to the specific data representation employed. In addition, the limited availability of EMG datasets, particularly those with high-quality labels, remains a critical bottleneck and continues to impede the translation of research advances into widespread real-world applications. We therefore examine emerging semi-supervised and self-supervised learning frameworks, which serve as complementary approaches to fully supervised paradigms. Finally, we outline promising future directions for the development of generalizable and robust deep learning for practical EMG decoding.