Jinqin Sheng
Papers
1
Total Citations
37
H-Index
1
About
Jinqin Sheng is a leading researcher in rehabilitation robotics and human–machine interaction, with a primary focus on the continuous control of upper limb assistive exoskeletons. Their most cited work, "Continuous Estimation of Human Upper Limb Joint Angles by Using PSO-LSTM Model" (2020, 37 citations), addresses a critical challenge in wearable robotics: accurately mapping surface electromyography (sEMG) signals to joint motion. By innovatively combining particle swarm optimization (PSO) with long short-term memory (LSTM) networks, Sheng developed a robust model that significantly improves the real-time estimation of human joint angles. This contribution bridges the gap between biological signals and mechanical actuation, enabling more natural and responsive exoskeleton control. Beyond this flagship study, Sheng’s research advances the fields of biosignal processing and intelligent control, with implications for stroke rehabilitation and assistive technologies. Their work is widely cited by engineers and clinicians developing next-generation wearable robots, underscoring its impact on both theoretical modeling and practical device design.
Research Focus
Key Achievements
Top Papers
- 1