Papers
3
Total Citations
28
H-Index
2
About
Sami Briouza is a researcher at the forefront of rehabilitation robotics and biomedical signal processing, specializing in the intersection of machine learning and human motor recovery. His work focuses on developing intelligent systems to restore arm and hand function using exoskeleton robots and electromyography (EMG) signals. Briouza’s major contributions include pioneering hybrid deep learning architectures for classifying surface EMG signals, such as a CNN-SVM model that achieved high accuracy in distinguishing upper-limb and hand movements for rehabilitation. His most cited paper, "A Brief Overview on Machine Learning in Rehabilitation of the Human Arm via an Exoskeleton Robot" (2021, 18 citations), provides a foundational survey that has guided subsequent research in assistive robotics. He further advanced the field with a CRNN-based classification approach (2023) for real-time arm rehabilitation. With a growing citation impact across his key works, Briouza is recognized for bridging computational methods with practical therapeutic applications, offering promising pathways for neuromuscular disorder patients and amputees. His research continues to shape the development of responsive, AI-driven exoskeletons that enhance motor recovery and quality of life.
Research Focus
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Top Papers
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