Papers
1
Total Citations
24
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About
Pengjie Qin is a leading researcher in the field of rehabilitation robotics and human motion intent recognition, with a primary focus on lower limb exoskeleton technologies. His work addresses a critical challenge in assistive robotics: enabling exoskeletons to intuitively and accurately interpret human movement intentions for seamless, flexible assistance. Qin’s major contribution is the development of a novel lower limb motion recognition method that integrates an improved wavelet packet transform with an unscented Kalman neural network. This approach significantly enhances the processing of surface electromyography (sEMG) signals, allowing for more precise and responsive control of lower extremity exoskeletons. His most-cited paper, published in 2020, has garnered 24 citations, reflecting its foundational impact on the field. By advancing sensor-based motion classification, Qin’s work paves the way for smarter, more adaptive assistive devices that can improve mobility and quality of life for individuals with lower limb impairments. His research is essential reading for engineers and scientists developing next-generation wearable robotic systems.
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