Muhammad Zia ur Rehman

National University of Sciences and Technology, Riphah International University

Papers

Total Citations

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About

Muhammad Zia ur Rehman is a leading researcher in biomedical engineering, specializing in myoelectric control systems and machine learning for prosthetic applications. His work focuses on advancing pattern recognition techniques for electromyographic (EMG) signals to improve the functionality and user acceptance of robotic hand prostheses. In his highly cited 2018 study (65 citations), Rehman conducted a comparative multi-day analysis of stacked sparse autoencoders for classifying hand motions using both surface and intramuscular EMG, addressing critical challenges in control robustness for wearable prosthetics. His 2019 work further explored the comparative performance of various classifiers for EMG signal analysis, contributing to the development of more reliable pattern recognition schemes for upper limb impairment and paralyzed individuals. By bridging deep learning with real-world prosthetic control, Rehman’s research has significant implications for enhancing the quality of life for amputees and individuals with motor disabilities. His contributions continue to shape the future of intelligent, adaptive prosthetic technologies.