Automated Grasp Recognition Using sEMG: Recent Advances, Challenges, and Future Developments

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 17
• Main Authors: Sharma, Shivam, Newaj Faisal, Kazi, Raj Sharma, Rishi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automated grasp recognition (AGR); Automatic control; Automation; deep neural networks (DNNs); Degrees of freedom; Electromyography; End effectors; Estimation; Feature extraction; Focusing; Force; Gesture recognition; Grasping (robotics); Industries; Machine learning; machine learning (ML); Man-machine interfaces; model-based approach; Multiresolution analysis; Prostheses; Reviews; Robot sensing systems; Sensors; State-of-the-art reviews; surface electromyography (sEMG); Virtual reality

Surface electromyography (sEMG)-based automated grasp recognition (AGR) has emerged as a vital technology in the field of automatic control, human-machine interfaces, prosthetics, virtual reality (VR), etc. Grasp recognition comes under the category of hand gesture recognition (HGR). However, due to...