Papers
3
Total Citations
28
H-Index
3
About
Haifeng Wu is a biomedical engineer and researcher specializing in human motion analysis, wearable robotics, and biosignal processing, with a particular focus on mechanomyography (MMG) signals and their applications in assistive technology. His work addresses a critical challenge in modern healthcare: providing effective motor assistance to elderly and disabled individuals suffering from motor dysfunction through intelligent wearable power-assisted robots. Wu's most significant contributions center on developing advanced machine learning frameworks for continuous human motion estimation. His 2020 study, which has garnered 19 citations, pioneered the application of Long Short-Term Memory (LSTM) neural networks to estimate knee joint acceleration from MMG signals, offering a powerful tool for real-time limb movement prediction. Complementing this, his CNN-SVM hybrid regression model demonstrated that continuous knee angle estimation — far more meaningful for robot control than discrete motion classification — could be achieved with high accuracy. Notably, Wu also explored the practical challenge of signal detection through clothing, enhancing the real-world viability of MMG-based wearable systems. With a growing citation record across multiple studies, Wu's research is making meaningful strides toward more natural, responsive assistive robotics for vulnerable populations.
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