  Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram
(sEMG) signals has recently shown significant potential for development of
advanced myoelectric-controlled prosthesis. Existing deep learning approaches,
typically, include only one model as such can hardly maintain acceptable
generalization performance in changing scenarios. In this paper, we aim to
address this challenge by capitalizing on the recent advances of hybrid models
and transformers. In other words, we propose a hybrid framework based on the
transformer architecture, which is a relatively new and revolutionizing deep
learning model. The proposed hybrid architecture, referred to as the
Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel
paths followed by a linear layer that acts as a fusion center to integrate the
advantage of each module and provide robustness over different scenarios. We
evaluated the proposed architecture TraHGR based on the commonly used second
Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset
are measured in the real-life conditions from 40 healthy users, each performing
49 gestures. We have conducted extensive set of experiments to test and
validate the proposed TraHGR architecture, and have compared its achievable
accuracy with more than five recently proposed HGR classification algorithms
over the same dataset. We have also compared the results of the proposed TraHGR
architecture with each individual path and demonstrated the distinguishing
power of the proposed hybrid architecture. The recognition accuracies of the
proposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are
2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance for
DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9
gestures), respectively.
