Yongkui Yang
Papers
1
Total Citations
37
H-Index
1
About
Yongkui Yang is a leading researcher in human–robot cooperation and biosignal-based control, with a focus on surface electromyography (sEMG) for continuous motion estimation. His work addresses a critical gap in man–machine interfaces: moving beyond discrete motion control toward seamless, natural, and accurate continuous control. Yang’s most-cited paper, “sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network” (2021, 37 citations), introduces a deep learning framework that decodes finger movements from muscle signals with high precision, advancing prosthetics and exoskeleton technologies. By leveraging large-scale temporal convolutional networks, he has demonstrated how neural architectures can capture the complex dynamics of muscle activation patterns, enabling real-time, fluid interaction between humans and robots. His contributions are pivotal for developing intuitive assistive devices and collaborative robots that respond to subtle user intent. With growing citation impact, Yang’s work continues to shape the field of intelligent human–machine systems, offering practical pathways toward more responsive and adaptive robotic interfaces.
Research Focus
Key Achievements
Top Papers
- 1