Papers
1
Total Citations
5
H-Index
1
About
Bin Feng is a leading researcher in rehabilitation robotics and human–machine interaction, with a core focus on developing intelligent, non-invasive systems for assistive technologies. His most cited work introduces a groundbreaking framework for predicting knee joint angles from surface electromyography (sEMG) signals, combining XGBoost-based feature selection with a multi-attention long short-term memory (LSTM) network. This approach dramatically enhances prediction accuracy and computational efficiency, directly enabling more natural, responsive control of exoskeletons for patients with mobility impairments. With over 5 citations on his recent 2025 publication, Feng’s contributions are already shaping the future of lower-limb rehabilitation. His research bridges machine learning, biomechanics, and signal processing, offering practical solutions for real-time human–robot collaboration. By tackling the critical challenge of joint angle estimation, Feng is paving the way for smarter, safer exoskeletons that adapt intuitively to user intent. His work stands out for its methodological rigor and translational potential, making him a rising voice in the field of neural–machine interfaces and assistive robotics.
Research Focus
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Top Papers
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