Liangqing Zhang

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About

Liangqing Zhang’s research lies at the intersection of biomedical engineering and rehabilitation robotics, with a primary focus on leveraging electromyography (EMG) signals for motor recovery in patients with neurological impairments. Her most cited work, “Pattern recognition based forearm motion classification for patients with chronic hemiparesis” (2013, 17 citations), demonstrates a pioneering application of pattern recognition techniques to decode multiple forearm motion classes from EMG data in chronic stroke survivors. This study was among the first to show that machine learning could reliably classify intended movements in hemiparetic patients, offering a pathway toward more intuitive, active rehabilitation devices. By translating rich muscular activity into actionable control signals, Zhang’s contributions have helped bridge the gap between neural intent and prosthetic or assistive device response. Her work is particularly notable for its direct clinical relevance, addressing the real-world challenge of restoring function in patients with limited voluntary movement. With a growing citation footprint, Zhang continues to influence the design of patient-specific, EMG-driven rehabilitation systems, making her a key voice in the effort to make neurorehabilitation smarter, more adaptive, and more effective.