Nan Bu

About

Nan Bu is a leading researcher in the field of human–robot interaction, with a primary focus on electromyography (EMG)-based control systems for prosthetic and assistive robotics. His work centers on developing intuitive, biomimetic interfaces that allow for seamless communication between human neural signals and robotic devices. Bu’s most influential contribution is his 2009 paper, "A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks," which has garnered 161 citations. This work introduced a novel task-modeling framework that combines Bayesian inference with neural networks to predict user intent, significantly enhancing the robustness and reliability of motion classification in prosthetic control. Building on this, his 2010 study on "Biomimetic Impedance Control of an EMG-Based Robotic Hand" demonstrated how raw EMG signals could be classified to achieve a natural, human-like impedance control for multi-joint robotic hands, achieving a more intuitive user experience. Through these contributions, Bu has advanced the practical viability of EMG-driven interfaces, bridging the gap between biological motor control and machine actuation for next-generation assistive technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks

   161 citations · 2009
2. 2
   Biomimetic Impedance Control of an EMG-Based Robotic Hand

   15 citations · 2010

Key Collaborators

Contact & Links