Surface EMG feature disentanglement for robust pattern recognition

Extracting robust features from surface electromyogram (sEMG) for accurate pattern recognition is a central research topic in biomechanics and human-machine interaction. Although related topics have been extensively investigated, the robustness of the recognition models over the inter-subject and inter-session signal variabilities remains challenging. From the perspective of feature projection, here we have proposed and validated the concept of sEMG feature disentanglement. We used an autoencoder-like architecture with specialized loss functions to explicitly decompose the sEMG features into the pattern-specific and subject-specific components. The former can be applied to robust sEMG pattern recognition, while the latter can be used as task-independent biometric identifiers. The proposed method was evaluated on data from twenty subjects with training and testing data acquired 3-25 days apart. The hand gesture recognition performance under the rigorous cross-subject and cross-day validation protocols demonstrates the proposed concept, showing a significant performance improvement over the state-of-the-art methods. Overall, this work provides a new insight into developing robust sEMG-based pattern recognition models. Moreover, it also indicates several exciting research directions in sEMG analysis, like task-independent sEMG biometric, sEMG privacy-preserving, and sEMG style-transfer.