Zhili Ruan
Papers
1
Total Citations
43
H-Index
1
About
Zhili Ruan is a leading researcher in biomedical signal processing and intelligent rehabilitation robotics, with a particular focus on muscle fatigue analysis and human–robot interaction. His most-cited work, “sEMG-Based Dynamic Muscle Fatigue Classification Using SVM With Improved Whale Optimization Algorithm” (2021, 43 citations), addresses a critical safety challenge in robot-assisted rehabilitation: the timely detection of muscle fatigue to prevent injury. Ruan pioneered the use of surface electromyography (sEMG) signals for dynamic fatigue classification, a problem that had seen limited success due to poor accuracy. By developing an improved whale optimization algorithm to tune support vector machine parameters, he achieved significantly higher classification performance, enabling real-time fatigue monitoring in dynamic rehabilitation scenarios. This contribution has direct implications for designing safer, more adaptive robotic therapy systems. Ruan’s work bridges machine learning and clinical rehabilitation, offering practical solutions for personalized patient care. His research continues to influence the development of intelligent assistive devices, with growing citation impact reflecting its relevance to both engineering and medical communities.
Research Focus
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Top Papers
- 1