Papers
2
Total Citations
14
H-Index
2
About
Yike Li is a researcher in wearable robotics and human-machine interaction, with a focus on sensing and control for assistive devices. Their work centers on using muscle deformations and joint synergy to estimate motion phases in real-world conditions, particularly for underwater exoskeletons and gait rehabilitation. Li’s most-cited paper, “Wearable Sensing for Breaststroke Phase Monitoring With Lower Limb Muscle-Joint Synergy” (2023, 10 citations), addresses the challenge of robust phase detection in harsh underwater environments—a critical step for synchronizing robotic assistance with human swimming motions. A subsequent study, “Continuous Gait Phase Estimation by Muscle Deformations With Speed and Ramp Adaptability” (2024, 4 citations), tackles the problem of asynchronous human-machine motion during walking, proposing a method that adapts to varying speeds and inclines to improve safety and efficiency. Though early in their career, Li’s contributions are notable for tackling real-world robustness issues in wearable robotics, bridging the gap between laboratory prototypes and practical deployment. Their work holds promise for advancing adaptive control in rehabilitation and performance enhancement.
Research Focus
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Top Papers
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