Guanghui Fan
Papers
1
Total Citations
19
H-Index
1
About
Guanghui Fan is a leading researcher in rehabilitation robotics and human motion analysis, with a primary focus on developing intelligent assistive technologies for lower limb impairment. His work integrates surface electromyography (sEMG) signal processing with robotic control systems to create adaptive rehabilitation solutions. Fan’s most cited study, "Lower limb motion recognition based on surface electromyography signals and its experimental verification on a novel multi-posture lower limb rehabilitation robot" (2022), has garnered 19 citations for its pioneering approach to decoding human intent from muscle activity. This research introduced a multi-posture robotic platform that can recognize and respond to user-specific motion patterns, significantly advancing the field of human-robot interaction in physical therapy. By combining real-time sEMG classification with mechanical design, Fan’s work bridges the gap between neural control and robotic assistance, offering personalized rehabilitation strategies. His contributions are particularly impactful for stroke survivors and spinal cord injury patients, where precise motion recognition is critical for effective therapy. Fan’s research continues to push boundaries in non-invasive neural interfaces and adaptive robotic systems, establishing him as a key innovator in the intersection of biomechanics and robotics.
Research Focus
Key Achievements
Top Papers
- 1