Yuefeng Li

Papers

Total Citations

110

H-Index

About

Yuefeng Li is a leading researcher in biomedical signal processing and human-machine interaction, with a focus on advancing assistive technologies and rehabilitation engineering. His work centers on developing novel sensing models for finger motion classification, addressing the limitations of traditional surface electromyography (sEMG) by exploring ultrasound-based approaches. His highly cited 2017 paper, "Ultrasound-Based Sensing Models for Finger Motion Classification" (110 citations), demonstrates how ultrasound imaging can capture the complex spatial and temporal coordination of forearm muscles and tendons, offering a more robust solution for dexterous hand gesture recognition. This contribution has significant implications for prosthetic control, robotic teleoperation, and human-computer interfaces. Li’s research bridges the gap between physiological sensing and practical motion decoding, providing a foundation for more intuitive and reliable control systems. His work is widely recognized for its potential to improve the quality of life for individuals with limb impairments, and he continues to influence the field through interdisciplinary collaborations and innovative sensing paradigms.