Ying Xuan Zhi
Papers
2
Total Citations
118
H-Index
2
About
Ying Xuan Zhi is a leading researcher in robotic stroke rehabilitation, with a focus on detecting and preventing compensatory movements that undermine therapy outcomes. Her work addresses a critical challenge in automated rehabilitation: when patients use unaffected muscles to compensate for impaired ones, they risk ineffective recovery and long-term maladaptive movement patterns. Zhi’s 2017 paper on automatic compensation detection during robotic stroke rehabilitation therapy (69 citations) introduced innovative algorithms to identify these harmful motion adaptations in real time, enabling corrective feedback during therapy sessions. She further advanced the field by creating the Toronto Rehab Stroke Pose Dataset (49 citations), a foundational resource that provides annotated movement data from stroke survivors performing rehabilitation exercises. This dataset has become an essential benchmark for developing machine learning models to distinguish between healthy and compensatory movement patterns. Zhi’s contributions bridge robotics, clinical rehabilitation, and computer vision, offering practical tools to enhance therapy efficiency and patient outcomes. Her work is widely cited by researchers developing autonomous rehabilitation systems and has significant implications for improving the quality of care in stroke recovery.
Research Focus
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Top Papers
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