Jiang Xiao
Papers
2
Total Citations
21
H-Index
2
About
Jiang Xiao is a researcher specializing in human motion analysis, rehabilitation engineering, and sensor-based gait recognition. Their major contributions lie in developing advanced algorithms for accurate gait phase detection, a critical component for improving exoskeleton-assisted walking and rehabilitation technologies. Notably, Xiao’s 2019 paper on a deep learning algorithm using multisensor information fusion has garnered 19 citations, demonstrating its impact on the field. This work introduced a novel approach that integrates inertial and plantar pressure data to enhance gait phase recognition, offering significant benefits for assisted rehabilitation. Additionally, Xiao proposed a new Hidden Markov Model algorithm for gait phase detection based on information fusion, further advancing the precision of motion analysis. By combining deep learning with multisensor fusion, Xiao has addressed key challenges in real-time gait monitoring, enabling more responsive and effective exoskeleton control. Their research is instrumental for students and researchers working on wearable robotics, human–machine interfaces, and biomechanics, providing practical solutions for analyzing human motion and supporting mobility-impaired individuals.
Research Focus
Key Achievements
Top Papers
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