Kaibin Chen
Papers
5
Total Citations
84
H-Index
5
About
Kaibin Chen is a researcher at the forefront of human-robot interaction, specializing in the estimation and control of human motion for safe and intuitive cooperative manipulation. His work bridges biomechanics and robotics, with a primary focus on using surface electromyography (sEMG) signals to decode human intent. In his most cited work (38 citations), Chen developed a method for estimating elbow joint motion from sEMG by fusing continuous wavelet transforms with backpropagation neural networks, achieving high-accuracy, non-invasive motion prediction. He further advanced this line of research by incorporating autoencoders to estimate joint torque, demonstrating how deep learning can extract robust features from noisy biological signals. Beyond signal processing, Chen has made notable contributions to hardware design for compliant robotics. He pioneered a novel flat torsional spring with corrugated flexible units for Series Elastic Actuators (SEAs), a key technology for ensuring safety in physical human-robot interaction. His designs, detailed in papers with over a dozen combined citations, offer a compact, high-performance solution for torque sensing in cooperative robots. By integrating intelligent signal processing with novel mechanical compliance, Chen’s work is paving the way for robots that can truly work alongside humans, adapting to their movements and forces in real time.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5