  Advances in biosignal signal processing and machine learning, in particular
Deep Neural Networks (DNNs), have paved the way for the development of
innovative Human-Machine Interfaces for decoding the human intent and
controlling artificial limbs. DNN models have shown promising results with
respect to other algorithms for decoding muscle electrical activity, especially
for recognition of hand gestures. Such data-driven models, however, have been
challenged by their need for a large number of trainable parameters and their
structural complexity. Here we propose the novel Temporal Convolutions-based
Hand Gesture Recognition architecture (TC-HGR) to reduce this computational
burden. With this approach, we classified 17 hand gestures via surface
Electromyogram (sEMG) signals by the adoption of attention mechanisms and
temporal convolutions. The proposed method led to 81.65% and 80.72%
classification accuracy for window sizes of 300ms and 200ms, respectively. The
number of parameters to train the proposed TC-HGR architecture is 11.9 times
less than that of its state-of-the-art counterpart.
