Sean Rich U. Uy
Papers
1
Total Citations
7
H-Index
1
About
Sean Rich U. Uy is a researcher at the forefront of computational approaches to robotic stroke rehabilitation, where his work addresses critical challenges in therapy delivery and data analysis. His primary research areas include machine learning for healthcare, imbalanced learning, outlier detection, and assistive robotics. Uy’s major contribution lies in developing robust algorithms to detect compensation movements during robotic stroke therapy—a key issue that undermines treatment efficacy when patients unknowingly use incorrect muscle groups. His most cited paper, “Analysis of Detecting Compensation for Robotic Stroke Rehabilitation Therapy using Imbalanced Learning and Outlier Detection” (2020, 7 citations), pioneers methods to identify these subtle, erroneous motions from limited and skewed datasets, making automated therapy more reliable and accessible. This work directly tackles the high costs and geographic barriers of traditional stroke rehabilitation by enabling more precise, data-driven robotic interventions. Uy’s research has significant implications for improving motor recovery outcomes, and his innovative use of imbalanced learning techniques sets a foundation for future advancements in intelligent rehabilitation systems. His contributions are vital for students and researchers working at the intersection of robotics, clinical therapy, and machine learning.
Research Focus
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Top Papers
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