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Development of advance surface Electromyogram (sEMG)-based Human-MachineInterface (HMI) systems is of paramount importance to pave the way towardsemergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, themain focus of recent literature was on development of different Deep NeuralNetwork (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) haveresulted in a surge of significant interest on sEMG decomposition techniques toextract microscopic neural drive information. However, due to complexities ofsEMG decomposition and added computational overhead, HGR at microscopic levelis less explored than its aforementioned DNN-based counterparts. In thisregard, we propose the HYDRA-HGR framework, which is a hybrid model thatsimultaneously extracts a set of temporal and spatial features through its twoindependent Vision Transformer (ViT)-based parallel architectures (the socalled Macro and Micro paths). The Macro Path is trained directly on thepre-processed HD-sEMG signals, while the Micro path is fed with the p-to-pvalues of the extracted Motor Unit Action Potentials (MUAPs) of each source.Extracted features at macroscopic and microscopic levels are then coupled via aFully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGRframework through a recently released HD-sEMG dataset, and show that itsignificantly outperforms its stand-alone counterparts. The proposed HYDRA-HGRframework achieves average accuracy of 94.86% for the 250 ms window size, whichis 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively.

HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information