Ziniu Zeng

Papers

Total Citations

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About

Ziniu Zeng is a leading researcher in rehabilitation robotics and human–machine interaction, with a focus on developing intelligent exoskeletons for upper limb recovery after neurological injury. His work integrates surface electromyography (sEMG) signal processing, attention-based deep learning, and compliant actuation to create adaptive, patient-responsive rehabilitation systems. In his highly cited 2022 paper, Zeng introduced a motion intention prediction framework for stroke survivors using sEMG and attention mechanisms, achieving 18 citations for its novel approach to decoding user intent. He also designed and evaluated a parallel cable-driven shoulder mechanism with series springs (13 citations), demonstrating how lightweight, low-cost cable-driven exoskeletons can provide safe, compliant assistance. Additionally, his 2022 work on an elbow rehabilitation exoskeleton with sEMG-based torque estimation (5 citations) advanced human–computer interaction by enabling real-time torque control tailored to varying forearm postures. Zeng’s contributions are pivotal in bridging biosignal sensing and robotic actuation, offering scalable solutions for personalized neurorehabilitation. His research continues to shape the future of assistive robotics, making therapy more accessible and effective for survivors of stroke and other movement disorders.