Yilun Cai
Papers
1
Total Citations
43
H-Index
1
About
Yilun Cai’s research lies at the intersection of biomedical signal processing and intelligent rehabilitation robotics, with a primary focus on muscle fatigue detection using surface electromyography (sEMG). His most influential work, “sEMG-Based Dynamic Muscle Fatigue Classification Using SVM With Improved Whale Optimization Algorithm” (2021, 43 citations), addresses a critical safety gap in robot-assisted rehabilitation: the failure to detect muscle fatigue in real time can lead to muscle damage. Cai pioneered a novel classification framework that combines support vector machines with an improved whale optimization algorithm, achieving significantly higher accuracy in dynamic fatigue classification—a challenge that had previously yielded unsatisfactory results. This contribution has direct implications for designing safer, more responsive rehabilitation systems that adapt to a patient’s real-time muscular state. By advancing the use of sEMG for dynamic, rather than static, fatigue analysis, Cai has opened new pathways for human-robot interaction in clinical and assistive technologies. His work is particularly notable for its practical impact on patient safety and its methodological innovation in optimizing machine learning models for physiological signals.
Research Focus
Key Achievements
Top Papers
- 1