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The 34th HSN 2024 HSN »õ·Î¿î½ÃÀÛ:
Hyper_converged Services and iNfrastructures
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The 34th HSN 2024 HSN »õ·Î¿î½ÃÀÛ:
Hyper_converged Services and iNfrastructures
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ÃÊû¹ßÇ¥A : ÁÂÀå :
¹ßÇ¥Á¦¸ñ : Leveraging Wearable Tech and Machine Learning for Digital Motor Phenotyping
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¼Ò¼Ó : UMASS Amherst ºÎ¼­ : Computer Science
Á÷À§ : Associate Professor ¹ßÇ¥ÀϽà : 1/25(¸ñ) 11:00~11:40
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Ivan Lee is a Donna M and Robert J Manning Faculty Fellow and an Associate Professor of Computer Science and of Electrical and Computer Engineering at UMass Amherst. He received his PhD in Computer Science, MS in Computer Science, and MS in Electrical Engineering, all from the University of California Los Angeles in 2010, 2013, and 2014, respectively. From 2014 to 2016, he was a post-doctoral fellow in the Department of Physical Medicine & Rehabilitation at Harvard Medical School.

Ivan is a recipient of the NSF CRII Award and the NIH Trailblazer Award for young investigators. He is currently an Editor for the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) and an Associate Editor for the ACM Transactions on Computing for Healthcare (HEALTH)and the IEEE Open Journal of Engineering in Medicine and Biology (OJEMB). He is a Senior Member of the IEEE and an elected Member of the Technical Committee on Wearable Biomedical Sensors and Systems of the IEEE Engineering in Medicine and Biology Society (EMBS). Ivan has served as the General Conference Chair for several premier conferences in the field, including ACM SenSys in 2022 and IEEE BSN in 2023. Ivan has also served as the co-chair of an NIH study section for the NICHD-NCMRR Early Career Research Award in 2022. Ivan has earned recognition within UMass Amherst with the Armstrong Research Award, the Lilly Fellowship for Teaching Excellence, and the ADVANCE Fellowship for Faculty Equity.
°­¿¬¿ä¾à :
Stroke affects a significant number of individuals in the United States, with nearly 800,000 cases reported annually, leading to substantial disability. Almost 80% of stroke survivors experience chronic motor and cognitive impairments, severely impacting their daily lives and overall well-being. The integration of mobile technologies, including smartphones and wearable devices, in conjunction with advanced machine learning algorithms, has emerged as a potent tool for gathering critical personal data pertinent to the functional status and performance levels in stroke survivors. In this presentation, I will share our research team's recent endeavors, which aim to (1) gain insights into patients' motor behaviors beyond clinical settings and (2) offer data-driven technological interventions to facilitate self-directed rehabilitation management.
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