  Development of advance surface Electromyogram (sEMG)-based Human-Machine
Interface (HMI) systems is of paramount importance to pave the way towards
emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the
main focus of recent literature was on development of different Deep Neural
Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR)
at a macroscopic level (i.e., directly from sEMG signals). At the same time,
advancements in acquisition of High-Density sEMG signals (HD-sEMG) have
resulted in a surge of significant interest on sEMG decomposition techniques to
extract microscopic neural drive information. However, due to complexities of
sEMG decomposition and added computational overhead, HGR at microscopic level
is less explored than its aforementioned DNN-based counterparts. In this
regard, we propose the HYDRA-HGR framework, which is a hybrid model that
simultaneously extracts a set of temporal and spatial features through its two
independent Vision Transformer (ViT)-based parallel architectures (the so
called Macro and Micro paths). The Macro Path is trained directly on the
pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p
values of the extracted Motor Unit Action Potentials (MUAPs) of each source.
Extracted features at macroscopic and microscopic levels are then coupled via a
Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR
framework through a recently released HD-sEMG dataset, and show that it
significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR
framework achieves average accuracy of 94.86% for the 250 ms window size, which
is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively.
