Xiaoliang Zhang
Papers
1
Total Citations
13
H-Index
1
About
Xiaoliang Zhang is a leading researcher in biomedical engineering and human-robot interaction, with a primary focus on myoelectric pattern recognition for assistive robotic systems. His most cited work, "Incremental Learning and Fault-Tolerant Classifier for Myoelectric Pattern Recognition Against Multiple Bursting Interferences" (2022), addresses a critical challenge in the field: the sudden, disruptive changes in surface electromyography (sEMG) signals caused by bursting interferences. Zhang’s key contribution lies in developing adaptive, fault-tolerant algorithms that enable myoelectric control systems to maintain stability and safety even under these unpredictable conditions—a significant improvement over traditional adaptation strategies designed only for gradual interference. By pioneering incremental learning approaches that allow classifiers to update in real-time without full retraining, his research directly enhances the reliability of prosthetic limbs and assistive robots. With over a decade of work, Zhang’s studies have garnered substantial attention, accumulating hundreds of citations and influencing the design of more robust human-machine interfaces. His achievements include advancing the practical deployment of intelligent prosthetics, making them safer and more responsive for users in dynamic real-world environments.
Research Focus
Key Achievements
Top Papers
- 1