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About

Abdussalam Ali Ahmed is a rising researcher at the intersection of rehabilitation robotics, embedded machine learning, and neural network applications. His primary focus lies in developing intelligent, low-cost assistive technologies for post-stroke recovery and mobility restoration. Ahmed’s most impactful work, “Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi” (2023, 16 citations), addresses a critical clinical challenge: delayed rehabilitation causing muscle atrophy. By integrating a single-lead EMG sensor with on-device machine learning on a Raspberry Pi, he demonstrated a practical, real-time control system for upper-limb exoskeletons, significantly reducing hardware complexity. Complementing this, his review on deep learning and neural networks for prosthetics and exoskeletons (2023, 5 citations) provides a comprehensive synthesis of state-of-the-art methods and challenges, serving as a key resource for researchers. Ahmed also introduced a novel, low-cost algorithm for a physiotherapy robot targeting both upper and lower limbs (2022, 3 citations), emphasizing simplicity and affordability. His work is notable for bridging advanced computational methods with accessible, real-world rehabilitation solutions, making him a promising contributor to the future of assistive robotics.