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About

Stephen Lemos is a leading researcher in the field of assistive robotic technologies, with a primary focus on the intersection of surface electromyography (sEMG), electroencephalography (EEG), and machine learning for rehabilitative exoskeleton control. His work addresses critical challenges in decoding human motion intent from biosignals to enable intuitive, real-time control of upper-limb assistive devices. Lemos's most impactful contribution is his 2020 study on shoulder muscle activation pattern recognition using sEMG and machine learning algorithms, which has garnered 72 citations and established foundational methods for volitional control of robotic exoskeletons. He further advanced the field with his highly cited work on processing sEMG signals for exoskeleton motion control (56 citations), which introduced novel procedures for improving system accuracy and reducing noise. Lemos has also pioneered the use of deep learning for EEG-based hand motion pattern recognition, demonstrating the feasibility of extracting complex limb motion intents from brain signals. His research portfolio includes the development and validation of a novel upper-limb exoskeleton for robotic assistive surgery and stroke rehabilitation, as well as real-time multiple-channel EMG processing using artificial neural networks. Through these contributions, Lemos is driving the evolution of intelligent, responsive assistive technologies that can restore mobility and independence to individuals with neurological impairments.