Papers
1
Total Citations
4
H-Index
1
About
Ye Ye is a leading researcher in rehabilitation robotics and human–machine interaction, with a core focus on decoding motor intent from surface electromyography (sEMG) signals. Their most cited work, “Online pattern recognition of lower limb movements based on sEMG signals and its application in real-time rehabilitation training” (2023), introduces a personalized, real-time method for classifying lower-limb motions using wearable wireless sensors. This contribution directly addresses a critical bottleneck in neurorehabilitation: translating raw muscle activity into responsive, patient-specific robotic assistance. By demonstrating online pattern recognition in practical training scenarios, Ye’s research bridges the gap between laboratory algorithms and clinical usability. Their work has garnered early citations from peers in biomechatronics and assistive technology, signaling growing influence in the field. Ye’s achievements include the development of a custom wireless acquisition instrument tailored for rehabilitation settings, showcasing a rare combination of hardware and algorithmic innovation. For students and researchers, Ye’s trajectory exemplifies how integrating signal processing, wearable sensing, and clinical insight can advance real-world rehabilitation outcomes.
Research Focus
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Top Papers
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