Lightweight Deep Learning for sEMG-Based Fingers Position Classification and Embedded System Deployment

Lightweight Deep Learning for sEMG-Based Fingers Position Classification and Embedded System Deployment

This paper presents a lightweight deep learning approach for classifying and tracking finger positions based on surface electromyography (sEMG) signals, designed with a perspective of its implementation in embedded systems. Unlike traditional studies that focus on static hand gestures, this research...

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Bibliographic Details
Main Authors: Victor H. Benitez, Jesus Pacheco, Guillermo Cuamea-Cruz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Artificial intelligence
embedded systems
finger position tracking
lightweight deep learning
real-time classification
sEMG signal analysis
Online Access:https://ieeexplore.ieee.org/document/10904232/
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https://ieeexplore.ieee.org/document/10904232/

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