Wennan Chang
Papers
3
Total Citations
201
H-Index
3
About
Wennan Chang is a leading researcher at the intersection of neural engineering and rehabilitation robotics, with a primary focus on decoding human motor intent for advanced prosthetic and assistive systems. His most influential work, an sEMG-based hand motion identification system (185 citations), pioneered the integration of wavelet neural networks with discrete wavelet transforms to dramatically improve the dexterity of myoelectric prosthetic hands—a breakthrough that directly enhances the quality of life for amputees by enabling more natural, intuitive control. Chang further expanded the boundaries of human-machine interaction by designing an SSVEP-based brain-computer interface (BCI) that allows users to command a service robot through visual attention alone, demonstrating the feasibility of hands-free control for physically challenged individuals. His innovative upper limb training system, which fuses sEMG and IMU sensors with a UR5 robot arm, addresses a critical gap in rehabilitation by enabling remote, professional-grade therapy even when expert trainers are unavailable. Through these contributions—spanning signal processing, wearable sensors, and robotic control—Chang has established himself as a key figure in making assistive technologies more responsive, accessible, and clinically viable.
Research Focus
Key Achievements
Top Papers
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