{"640132":{"#nid":"640132","#data":{"type":"event","title":"PhD Proposal by Hyeokhyen Kwon","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Opportunistic Use of Video Data For Wearable-based Human Activity Recognition\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EHyeokhyen Kwon\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPh.D. student in Computer Science\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Interactive Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECollege of Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: Monday, October 19\u003Csup\u003Eth\u003C\/sup\u003E, 2020\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 10:00 AM to 12:00 PM (EST)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/bluejeans.com\/3537894193\u0022\u003Ehttps:\/\/bluejeans.com\/3537894193\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Gregory D. Abowd (Advisor) \u0026ndash; School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Thomas Ploetz (Co-Advisor) \u0026ndash; School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Thad Starner \u0026ndash; School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Irfan Essa \u0026ndash; School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Nicholas D. Lane \u0026ndash; Dept. of Computer Science \u0026amp; Tech., University of Cambridge\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWearable Inertial Measurement Unit (IMU)-based human activity recognition is at the core of continuous monitoring for human well-being, which can detect precursors of health risks in everyday life. Conventionally, wearable sensor data is collected from recruited users, where user engagement is expensive, and the annotation is time-consuming. \u0026nbsp;Due to the lack of large-scale labeled datasets, the wearable-based human activity recognition model has yet to experience significant improvements in recognition performance. To tackle the scale limitations in the wearable sensor dataset, this dissertation proposes a novel approach, which aims at harvesting existing video data from virtually unlimitedly large repositories, such as YouTube. I introduce an automated processing pipeline that integrates existing computer vision and signal processing techniques to convert human activity videos into virtual IMU data streams. I show how the virtually-generated IMU data improves the performance of various models on known human activity recognition datasets. I also proposed approaches to improve the quality of the generated virtual IMU data and decrease the domain gap between virtual and real IMU data. To further improve the recognition accuracy, I discuss a novel model training approach to handle human activity annotation noise in video datasets. This dissertation shows the promise of using video as a novel source for human activity recognition with wearables, representing a paradigm shift for deriving a robust human activity recognition system.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Opportunistic Use of Video Data For Wearable-based Human Activity Recognition"}],"uid":"27707","created_gmt":"2020-10-12 19:13:30","changed_gmt":"2020-10-12 19:13:30","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-10-19T11:00:00-04:00","event_time_end":"2020-10-19T13:00:00-04:00","event_time_end_last":"2020-10-19T13:00:00-04:00","gmt_time_start":"2020-10-19 15:00:00","gmt_time_end":"2020-10-19 17:00:00","gmt_time_end_last":"2020-10-19 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}