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Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram(sEMG) signals has recently shown significant potential for development ofadvanced myoelectric-controlled prosthesis. Existing deep learning approaches,typically, include only one model as such can hardly maintain acceptablegeneralization performance in changing scenarios. In this paper, we aim toaddress this challenge by capitalizing on the recent advances of hybrid modelsand transformers. In other words, we propose a hybrid framework based on thetransformer architecture, which is a relatively new and revolutionizing deeplearning model. The proposed hybrid architecture, referred to as theTransformer for Hand Gesture Recognition (TraHGR), consists of two parallelpaths followed by a linear layer that acts as a fusion center to integrate theadvantage of each module and provide robustness over different scenarios. Weevaluated the proposed architecture TraHGR based on the commonly used secondNinapro dataset, referred to as the DB2. The sEMG signals in the DB2 datasetare measured in the real-life conditions from 40 healthy users, each performing49 gestures. We have conducted extensive set of experiments to test andvalidate the proposed TraHGR architecture, and have compared its achievableaccuracy with more than five recently proposed HGR classification algorithmsover the same dataset. We have also compared the results of the proposed TraHGRarchitecture with each individual path and demonstrated the distinguishingpower of the proposed hybrid architecture. The recognition accuracies of theproposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance forDB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9gestures), respectively.

TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography