Yucheng Long
Papers
2
Total Citations
87
H-Index
2
About
Yucheng Long is a leading researcher in human-machine interfaces, specializing in the continuous estimation of finger kinematics from surface electromyography (sEMG) signals. His work bridges the gap between biological motor intent and robotic dexterity, with profound implications for rehabilitation and industrial applications. Long’s most cited paper, “A CNN-Attention Network for Continuous Estimation of Finger Kinematics from Surface Electromyography” (2022, 58 citations), introduces a novel deep learning architecture that significantly improves the precision of simultaneous and proportional control for robotic hands. This work addresses a critical challenge in enabling natural, intuitive interaction between humans and machines. Building on this, his 2023 paper (29 citations) pioneers a transfer learning approach to create cross-subject generic models, eliminating the need for lengthy calibration when a new user operates the system. By allowing seamless adaptation across individuals, Long’s contributions accelerate the deployment of sEMG-based prosthetics and exoskeletons. His research not only advances neural decoding techniques but also sets a foundation for accessible, user-friendly assistive technologies. With growing citation impact, Yucheng Long is shaping the future of intelligent, human-centered robotics.
Research Focus
Key Achievements
Top Papers
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