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Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography. (arXiv:2210.15119v1 [cs.LG])
Oct. 28, 2022, 1:11 a.m. | Soheil Zabihi, Elahe Rahimian, Amir Asif, Arash Mohammadi
cs.LG updates on arXiv.org arxiv.org
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, …
More from arxiv.org / cs.LG updates on arXiv.org
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