Papers
1
Total Citations
4
H-Index
1
About
Zhipeng Deng is a researcher focused on advancing rehabilitation robotics and human-machine interaction, with a particular emphasis on gait training for individuals with neurological impairments. His work centers on developing intelligent control strategies for body-weight-support (BWS) treadmill training systems, addressing critical challenges in post-stroke rehabilitation. Deng’s most cited paper, “A Center of Mass Estimation and Control Strategy for Body-Weight-Support Treadmill Training” (2021), introduces a novel approach to dynamically estimate and control the center of mass during walking. This contribution directly tackles the limitations of fixed weight support and constant walking speed, which often reduce patient engagement and increase fall risk. By enabling adaptive support that responds to the user’s real-time movements, Deng’s work enhances both safety and active participation in therapy. With 4 citations, this paper represents a foundational step toward more responsive, patient-centered rehabilitation technologies. Deng’s research bridges biomechanics, control systems, and clinical application, offering promising pathways for improving motor recovery outcomes in stroke survivors and other populations with walking disorders.
Research Focus
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Top Papers
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