Bingjing Guo
Papers
11
Total Citations
115
H-Index
5
About
Bingjing Guo is a robotics and rehabilitation engineering researcher whose work centers on the design and intelligent control of assistive robotic systems for motor recovery. With a career spanning nearly a decade, Guo has made significant contributions to both lower and upper limb rehabilitation robotics, developing novel exoskeleton platforms, end-effector robots, and human-robot interaction frameworks tailored to clinical and bedside settings. His most influential work, a 2019 study on reinforcement learning-based interactive control for gait rehabilitation exoskeletons (51 citations), demonstrated how adaptive algorithms can personalize assistance for patients during training — a landmark step toward truly responsive rehabilitation systems. His earlier horizontal lower limb rehabilitation robot (2016, 20 citations) addressed the practical needs of bedridden patients, creatively integrating traditional Chinese medicine massage techniques into robotic design. More recently, Guo has explored assist-as-needed control strategies that leverage patients' healthy-limb motion data to drive progressive recovery, alongside innovative training paradigms inspired by Tai Chi. His research bridges mechanical design, control theory, and clinical rehabilitation, offering tools that enhance patient engagement and functional recovery across a range of neurological conditions.
Research Focus
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Top Papers
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