[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125490-en":3,"doc-seo-125490-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125490,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","MACHINE LEARNING ENHANCEMENTS FOR WEARABLE DEVICE INVESTIGATION OF ACUTE ANXIETY AND CARDIOVASCULAR DISEASES - Dissertation","Promoting well-being and healthy ageing in young adults increasingly hinges on detecting and managing conditions that silently erode quality of life, chief among them cardiovascular health and anxiety-related disorders. This dissertation advances both fields by developing data-driven methods that move clinical assessment from infrequent clinic visits to continuous, patient-centred monitoring. It addresses early autonomic markers of cardiovascular health and context-specific anxiety surges. By integrating multimodal wearable signals with modern machine learning, the work supports risk forecasting, just-in-time interventions, and improved monitoring for healthier, more independent ageing.","© 2025 Ayse Dogan  \nMACHINE LEARNING ENHANCEMENTS FOR WEARABLE DEVICE INVESTIGATION OF ACUTE ANXIETY AND CARDIOVASCULAR DISEASES  \nBY  \nAYSE DOGAN  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Industrial Engineering in the Graduate College of the University of Illinois Urbana-Champaign, 2025  \nUrbana, Illinois  \nDoctoral Committee:  \nProfessor Richard B. Sowers, Chair  \nTeaching Associate Professor Manuel E. Hernandez  \nProfessor Caroline Cao  \nProfessor Ramavarapu S. Sreenivas  \nAbstract  \nPromoting well-being and healthy ageing in young adults increasingly hinges on detecting and managing conditions that silently erode quality of life, chief among them cardiovascular health and anxiety-related disorders. This dissertation advances both fields by developing data-driven methods that move clinical assessment from infrequent clinic visits to continuous, patient-centred monitoring. We address two persistent challenges: (i) identifying early autonomic markers of cardiovascular health before life-threatening damage occurs; and (ii) recognising context-specific surges in anxiety that impair daily functioning yet often escape formal diagnosis. By integrating multimodal wearable signals with modern machine-learning techniques, the work demonstrates how artificial intelligence can support clinicians in forecasting cardiovascular risk, delivering just-in-time interventions for anxiety, and ultimately fostering healthier, more independent ageing.  \nThis work proposes new data-driven machine learning-based solutions utilizing health data from multiple modalities, such as electrocardiography (ECG), blood volume pulse (BVP), electrodermal activity (EDA) etc. signals, to improve early disease prediction and progression in physical and mental health disorders. We measure our ability to use these signals to classify anomalies in the cardiovascular health and physiological body reactions in persons with disorders. This thesis is a multidisciplinary effort that involves novel combinations of sensors, audio, machine learning, biomechanics, and dynamical analyses to better characterize physiological signals from the body. These studies on the integration of AI and health data may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. This may help providers to individualize or generalize treatment plans and design improved clinical trials; thus, help reduce the skyrocketing healthcare costs in the future.  \nThe focus of this dissertation is on the following three areas under the broad umbrella of AI for digital healthcare: 1) Feature transformation for ECG signals so that we can get information about the cardiovascular risk in an automated settings, 2) Understanding the lab data collection for the mental health research settings i.e., anxiety and multimodal data analysis for the detection of state anxiety, where we focus on earlystage disease detection and propose feature engineering settings with machine learning models to identify physiological reactions for state anxiety, and 3) Transforming what we learned from the lab (controlled environment settings) to the daily life settings (uncontrolled environment settings) through wearable devices.  \nTo my family, friends, and mentors for their love and support.  \niii  \nAcknowledgments  \nThis project would not have been possible without the support of many people. I would first like to express my deepest gratitude to my advisor, Professor Richard B. Sowers, for his continuous guidance, patience, and encouragement throughout my Ph.D. journey. His inspiring perspective, invaluable insights, and countless discussions have shaped my way of thinking as an independent researcher. I have learned an entirely new mindset during this training, one that will guide me in solving any problem throughout my life. This dissertation is possible only because","cbCaik4ALab8ZyCl","https://ap.wps.com/l/cbCaik4ALab8ZyCl","pdf",10765340,1,84,"English","en",105,"# Abstract\n## Multimodal wearable data and machine learning\n## Challenges: cardiovascular markers and anxiety detection\n## Three dissertation focus areas\n## Applications for clinical prediction and monitoring","[{\"question\":\"What two main challenges does the dissertation address?\",\"answer\":\"It targets (i) identifying early autonomic markers of cardiovascular health before severe damage occurs, and (ii) recognizing anxiety surges that disrupt daily functioning but are often missed by formal diagnosis.\"},{\"question\":\"Which wearable signals and modalities are used in the proposed methods?\",\"answer\":\"The work uses multiple modalities including electrocardiography (ECG), blood volume pulse (BVP), and electrodermal activity (EDA), integrating them for physiological analysis.\"},{\"question\":\"How does the dissertation translate findings from controlled labs to daily life?\",\"answer\":\"It focuses on transforming knowledge learned in controlled environments into uncontrolled, real-world daily-life settings through wearable devices, enabling continuous patient-centred monitoring.\"}]","MACHINE LEARNING ENHANCEMENTS FOR WEARABLE DEVICE INVESTIGATION OF ACUTE ANXIETY AND CARDIOVASCULAR DISEASES - 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