Zhenhong Liang
Papers
1
Total Citations
18
H-Index
1
About
Zhenhong Liang is a leading researcher in rehabilitation robotics and human–machine interaction, with a primary focus on decoding human motion intent for assistive technologies. Their most cited work, "Motion intention prediction of upper limb in stroke survivors using sEMG signal and attention mechanism" (2022, 18 citations), introduces a novel deep learning framework that integrates surface electromyography (sEMG) with an attention mechanism to accurately predict upper-limb movement intentions in stroke survivors. This contribution is pivotal for developing responsive, patient-specific rehabilitation exoskeletons and prosthetics, directly addressing the challenge of real-time, non-invasive control in clinical and home settings. Liang’s research bridges signal processing, machine learning, and neurorehabilitation, demonstrating how attention-based models can enhance the robustness of intent prediction despite noisy biological signals. With a growing citation record, their work has influenced both engineering design and therapeutic practice, offering a pathway toward more intuitive and adaptive assistive devices. Liang’s achievements underscore a commitment to translating computational advances into tangible improvements for individuals with motor impairments, making their research highly relevant for students and engineers working at the intersection of AI and healthcare.
Research Focus
Key Achievements
Top Papers
- 1