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What is sEMG Control for Prosthetic Hands about?
This paper presents a novel approach for controlling a prosthetic hand using only two low-cost surface EMG sensors and an embedded deep learning model, achieving 93% accuracy in identifying seven different grasps and gestures. The system minimizes data acquisition needs and is implemented in an enhanced version of the OLYMPIC prosthetic hand, allowing for instinctive multi-grasp control in real-world settings. The research addresses the limitations of existing myoelectric prostheses by reducing costs and co
- Author
- 22f1001344
- Language
- EN