Yaser Mowafi
Papers
2
Total Citations
12
H-Index
2
About
Yaser Mowafi is a researcher whose work sits at the intersection of biomedical engineering, rehabilitation robotics, and human-machine interaction. His primary research focus is on developing robust, non-invasive methods for decoding human hand and finger kinematics from surface electromyography (sEMG) signals—a critical step toward creating intuitive, high-degree-of-freedom (DOF) prosthetic hands that can restore natural function for amputees. Mowafi’s major contributions include pioneering the use of wavelet-based feature extraction combined with support vector regression (SVR) to continuously estimate hand joint angles, moving beyond simple discrete gesture classification to enable fluid, proportional control. His 2016 paper on an ensemble-based regression approach for wrist and finger movement estimation (7 citations) and his work on wavelet features for continuous joint angle estimation (5 citations) are foundational, demonstrating how machine learning can bridge the gap between biological signals and robotic actuation. By advancing the accuracy and responsiveness of myoelectric control systems, Mowafi’s research directly contributes to improving the quality of life for individuals with limb loss, pushing the boundaries of what is possible in assistive technology and neural-machine interfaces.
Research Focus
Key Achievements
Top Papers
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