Jinqiang Wang

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About

Jinqiang Wang is an emerging researcher specializing in biomedical signal processing, human-robot interaction, and intelligent rehabilitation engineering. His work centers on leveraging surface electromyography (sEMG) signals to decode human movement intent, with a particular focus on joint angle estimation for exoskeleton and rehabilitation robot applications. Wang's most notable contributions include developing a state-space model driven by sEMG signals for continuous joint angle estimation, integrating Hill-based muscle modeling with forward dynamics algorithms to improve biomechanical accuracy. Complementing this, his research on predicting elbow joint angles from sEMG signals directly addresses a critical challenge in rehabilitation robotics — enabling robots to respond in real-time to a patient's movement intentions with greater precision and personalization. Though early in his publishing career, Wang's work has already garnered 6 citations across two 2024 publications, reflecting growing interest in his approaches within the rehabilitation and human-robot interaction communities. His research holds significant promise for advancing exoskeleton technology and personalized rehabilitation systems, potentially improving outcomes for patients with motor impairments. Students and researchers working at the intersection of biomechanics, neural engineering, and robotics will find his contributions particularly relevant and timely.