Papers
11
Total Citations
113
H-Index
5
About
Shixin Ren is a leading researcher in rehabilitation robotics and human-robot interaction, with a focus on developing intelligent systems for lower limb neurorehabilitation. Their work centers on three key areas: personalized gait training, human motion intention prediction, and adaptive control strategies for rehabilitation robots. Ren’s most impactful contribution is the use of Random Forest models to generate personalized gait trajectories based on anthropometric features (40 citations), enabling patient-specific training that adapts to individual biomechanics. They have also pioneered methods for predicting human voluntary torques using collaborative neuromusculoskeletal modeling and surface electromyography (sEMG) (24 citations), addressing a critical challenge in active rehabilitation. Ren’s research extends to deep learning approaches, such as CNN-LSTM networks for predicting joint angles from multi-band sEMG signals (7 citations), and the design of a multiposture robot for full-cycle lower limb rehabilitation (2024). Their work on interactive control methods, including fuzzy adaptive impedance control and damping-based speed adjustment, has advanced compliant and safe human-robot interaction. With over 100 total citations, Ren’s innovations are shaping the future of autonomous, patient-adaptive rehabilitation systems.
Research Focus
Key Achievements
Top Papers
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- 6Interactive control methods for rehabilitation robot5 citations · 2017
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