Papers
5
Total Citations
95
H-Index
3
About
Bailin He is an emerging researcher specializing in human-robot interaction, myoelectric control systems, and rehabilitation robotics, with a particular focus on lower-limb exoskeleton technologies for stroke and hemiplegic patients. His most influential contribution, "Metric Learning for Robust Gait Phase Recognition" (2022), introduced a novel metric learning-based temporal convolution network (ML-TCN) that harnesses surface electromyography (sEMG) signals to accurately decode user movement intention — a breakthrough that has already attracted 45 citations. Complementing this work, He developed a pioneering misclassification detection method for locomotion mode recognition, garnering 30 citations and addressing a critical safety challenge in exoskeleton control. His earlier research on real-time stability control via sEMG interfaces (17 citations) further demonstrated his commitment to making rehabilitation robots practically viable for paraplegic users. Across his portfolio, He has consistently pushed toward more comfortable, responsive, and reliable exoskeleton systems, as evidenced by his investigations into admittance and impedance control strategies. With a focused body of work accumulating nearly 100 citations in just a few years, Bailin He is establishing himself as a promising voice in intelligent rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
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- 4Admittance Control based Hemiplegic Exoskeleton Motion Control2 citations · 2022
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