Zeyi Li
Papers
1
Total Citations
2
H-Index
1
About
Dr. Zeyi Li is a rising researcher at the intersection of rehabilitation robotics and human–machine interaction, with a primary focus on decoding motor intent from high-density surface electromyography (sEMG) signals. In their most-cited work, “A CNN-Transformer Hybrid Network for Hand Gesture Classification based on High-Density sEMG” (2024), Li introduced a novel deep-learning architecture that fuses convolutional neural networks with transformer attention mechanisms. This hybrid model significantly improves the accuracy and robustness of hand gesture recognition, a critical capability for active rehabilitation training in patients with movement disorders. By enabling more intuitive and responsive control of assistive devices, Li’s contributions directly support the shift toward patient-driven therapy, where users engage more actively in their recovery. Although early in their career—with the flagship paper already garnering 2 citations—Li’s work demonstrates strong potential for impact in neurorehabilitation and prosthetic control. Their research not only advances signal-processing techniques but also addresses a pressing clinical need: restoring motor function through intelligent, adaptive human–robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1