Papers
5
Total Citations
74
H-Index
5
About
Dongdong Bu is a leading researcher in rehabilitation robotics and human–machine interfaces, with a focus on surface electromyography (sEMG)-based control for upper limb exoskeletons. His work addresses critical challenges in stroke rehabilitation, where traditional therapies are costly and limited by therapist availability. Bu’s major contributions include developing subject-independent continuous estimation of joint angles using multisource domain adaptation combined with BP neural networks (33 citations), and pioneering sEMG-based motion recognition with an improved Yolo-v4 algorithm (25 citations)—both enabling more intuitive, adaptive control of rehabilitation robots without complex feature extraction. He also designed a portable upper limb rehabilitation robot based on an embedded system, advancing accessibility for home-based therapy. Notably, Bu has tackled inter-subject variability in sEMG signals, a key barrier to clinical adoption, and proposed a radial basis function neural network control method for precise robot assistance. His work bridges machine learning, biomechanics, and embedded systems, with cumulative citations exceeding 70. Bu’s innovations are paving the way for affordable, intelligent rehabilitation devices that respond naturally to each patient’s muscle activity, making him a pivotal figure in restoring upper limb function after stroke.
Research Focus
Key Achievements
Top Papers
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- 3Embedded System-based a Portable Upper Limb Rehabilitation Robot6 citations · 2018
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