Shuangyuan Huang
Papers
7
Total Citations
240
H-Index
5
About
Shuangyuan Huang is a leading researcher in rehabilitation robotics, with a focus on intelligent, patient-adaptive systems for upper and lower limb recovery. Their work centers on integrating surface electromyography (sEMG) and deep learning to enable natural, real-time control of rehabilitation robots. Huang’s most impactful contribution is a 2019 study on SVM-based classification of sEMG signals for upper-limb self-rehabilitation training, which has garnered 112 citations and established a foundation for patient-cooperative robotic therapy. They further advanced the field by developing a continuous estimation model for upper limb joint angles using sEMG and deep learning (55 citations), enabling more intuitive human-machine interaction. A notable achievement is their 2020 work on real-time detection of compensatory patterns in stroke patients (39 citations), which addresses a critical barrier to effective home-based therapy. Huang has also explored fatigue-induced compensation (21 citations) and personalized passive training for lower limb robots. Their research, spanning both upper and lower extremity rehabilitation, has been cited over 240 times, reflecting its significant impact on developing safer, more effective robotic therapies that adapt to individual patient needs and reduce the risk of maladaptive recovery.
Research Focus
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Top Papers
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