Min Tang
Papers
1
Total Citations
3
H-Index
1
About
Min Tang’s research lies at the intersection of human motor learning, rehabilitation robotics, and multisensory feedback systems. Her work focuses on how error modulation—the strategic manipulation of visual and haptic feedback—can enhance skill acquisition and user motivation during robot-assisted training. In her highly cited 2023 study, she systematically investigated the effects of combining visual and haptic feedback fusion strategies on motor learning outcomes, revealing that carefully designed error-based feedback can significantly improve both performance and intrinsic motivation. This contribution is particularly impactful for neurorehabilitation, where personalized feedback is critical for patient engagement and recovery. Although early in her career, Tang’s work has already garnered attention for its practical implications in designing more effective rehabilitation interfaces. Her research bridges engineering, psychology, and clinical practice, offering a nuanced understanding of how feedback modalities interact to shape learning. By demonstrating that fusion strategies outperform single-modality feedback, Tang has opened new pathways for adaptive training systems in stroke recovery and skill training. Her findings are essential reading for researchers developing intelligent, user-centered rehabilitation technologies.
Research Focus
Key Achievements
Top Papers
- 1