  Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML)
models have paved the path for development of novel immersive Human-Machine
Interfaces (HMI). In this context, there has been a surge of significant
interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram
(sEMG) signals. This is due to its unique potential for decoding wearable data
to interpret human intent for immersion in Mixed Reality (MR) environments. To
achieve the highest possible accuracy, complicated and heavy-weighted Deep
Neural Networks (DNNs) are typically developed, which restricts their practical
application in low-power and resource-constrained wearable systems. In this
work, we propose a light-weighted hybrid architecture (HDCAM) based on
Convolutional Neural Network (CNN) and attention mechanism to effectively
extract local and global representations of the input. The proposed HDCAM model
with 58,441 parameters reached a new state-of-the-art (SOTA) performance with
82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying
17 hand gestures. The number of parameters to train the proposed HDCAM
architecture is 18.87 times less than its previous SOTA counterpart.
