Papers
19
Total Citations
403
H-Index
11
About
Oluwarotimi Williams Samuel is a leading researcher in rehabilitation robotics and human-machine interaction, with a primary focus on electromyogram (EMG)-based pattern recognition for prosthetic control and stroke rehabilitation. His major contributions center on developing robust and intuitive myoelectric control systems that can withstand real-world interference. Notably, his work on a SCA-LSTM deep learning approach for continuous joint angle estimation (80 citations) and his investigations into the co-existing impacts of dynamic factors on EMG prostheses (52 citations) have significantly advanced the field. Samuel has pioneered robust sparse representation methods for myoelectric control (46 citations) and postprocessing strategies to improve prosthetic control reliability (35 citations). His research also extends to novel feature extraction for deep learning-based estimation (32 citations) and decoding movement intent for stroke rehabilitation (23 citations). With over 340 citations across his top papers, Samuel's work is instrumental in bridging the gap between laboratory-based EMG control and practical, real-world applications. His achievements include developing methods to mitigate noise interference and enhance signal robustness, making him a key figure in advancing assistive technologies for individuals with limb impairments.
Research Focus
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