Fangzheng Wang
Papers
4
Total Citations
60
H-Index
3
About
Fangzheng Wang is a researcher specializing in human motion analysis, wearable sensing technologies, and exoskeleton robotics, with a particular focus on bridging intelligent algorithms with assistive rehabilitation engineering. His most influential work centers on developing advanced gait recognition systems that leverage multisensor fusion to accurately capture and interpret human movement. Wang's 2019 paper on Support Vector Machine-based gait recognition using wearable sensors has garnered 33 citations, reflecting the field's strong interest in robust, real-world motion classification methods. Complementing this, his deep learning approach to gait phase recognition — accumulating 19 citations — demonstrates his commitment to pushing algorithmic boundaries for rehabilitation and assisted-walking applications. Wang has also contributed to the mechanical design domain, proposing an improved wearable power knee exoskeleton that addresses structural and hydraulic system limitations. Further expanding his methodological repertoire, he has explored Hidden Markov Models combined with inertial and plantar pressure data for precise gait phase detection. Collectively, Wang's research offers meaningful advances toward more intelligent, responsive exoskeleton control systems, making his work particularly valuable for researchers and engineers working at the intersection of biomechanics, machine learning, and assistive robotics.
Research Focus
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Top Papers
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