Papers
1
Total Citations
24
H-Index
1
About
Jiaqing Zhu is a leading researcher in the field of rehabilitation robotics and human motion analysis, with a primary focus on lower limb exoskeleton technology. His most cited work, "Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network" (2020, 24 citations), addresses a critical challenge in assistive robotics: accurately recognizing human motion intent to enable more natural and responsive exoskeleton control. Zhu’s major contribution lies in developing advanced signal processing and machine learning techniques—specifically, combining improved wavelet packet transform with unscented Kalman neural networks—to decode complex electromyographic (sEMG) signals. This innovation allows for more precise identification of lower limb movements using fewer sensors, significantly enhancing the practicality and wearability of exoskeletons. By reducing sensor requirements while maintaining high recognition accuracy, Zhu’s work directly supports the creation of lighter, more comfortable assistive devices for individuals with mobility impairments. His research sits at the intersection of biomedical engineering, neural networks, and human-robot interaction, offering tangible pathways toward smarter, more intuitive rehabilitation technologies.
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Top Papers
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