Weihuang Dai
Papers
1
Total Citations
9
H-Index
1
About
Weihuang Dai is a researcher at the forefront of intelligent rehabilitation robotics, specializing in the integration of deep learning and adaptive control for human-robot interaction. His most cited work, "Deeply feature learning by CMAC network for manipulating rehabilitation robots" (2021, 9 citations), introduces a novel approach that combines cerebellar model articulation controller (CMAC) networks with deep feature learning to enhance the precision and adaptability of rehabilitation robots. This contribution addresses a critical challenge in assistive technology—enabling robots to learn and respond to complex, individualized patient movements in real time. Dai’s research bridges the gap between neural network theory and practical robotic control, offering a pathway toward more intuitive and effective rehabilitation devices. While his citation count reflects the emerging nature of this field, his work has already influenced subsequent studies on adaptive control for medical robotics. By advancing the synergy between deep learning and robotic manipulation, Dai is helping to shape the next generation of intelligent rehabilitation systems that can better support patients in regaining motor function.
Research Focus
Key Achievements
Top Papers
- 1