{"635307":{"#nid":"635307","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Jonathan Zia","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EOn the Assessment of Cardiomechanical Function via Wearable Systems: Harnessing Emergent Patterns and Dynamics for Robust Physiological Monitoring\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Omer Inan, ECE, Chair , Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Christopher Rozell, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mark Davenport, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mozziyar Etemadi, Northwestern\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Jin-Oh Hahn, University of Maryland\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe objective of this research is to provide a mathematical and conceptual foundation for the processing and analysis of cardiomechanical signals. We begin by exploring a potential clinical application of this technology, using a multi-modal wearable system to accurately track the progression toward hypovolemic shock in an animal model of hemorrhage. In this manner, we demonstrate the potential for cardiomechanical sensing to enable data-driven triage and management of trauma injury. Capturing these signals from wearable systems, however, is a difficult task, creating a barrier to widespread application. To enable more robust analysis of these signals, we begin by presenting a unified method of determining signal quality and localizing the position of the cardiomechanical sensors on the chest wall by analyzing population-level patterns in signal morphology. Next, we develop and explore the idea that observed cardiomechanical signals \u0026ndash; while noisy and complex in the time domain \u0026ndash; derive from a simple low-dimensional dynamic process. By understanding and modeling these dynamics, we may perform more robust extraction of physiological data from these signals, as well as enabling higher-level tasks such as algorithmic compensation for sensor misplacement.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"On the Assessment of Cardiomechanical Function via Wearable Systems: Harnessing Emergent Patterns and Dynamics for Robust Physiological Monitoring "}],"uid":"28475","created_gmt":"2020-05-12 20:31:58","changed_gmt":"2020-05-12 20:31:58","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-05-28T14:00:00-04:00","event_time_end":"2020-05-28T16:00:00-04:00","event_time_end_last":"2020-05-28T16:00:00-04:00","gmt_time_start":"2020-05-28 18:00:00","gmt_time_end":"2020-05-28 20:00:00","gmt_time_end_last":"2020-05-28 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}