[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122301-en":3,"doc-seo-122301-105":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":20,"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},122301,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","WEARABLE SYSTEMS AND MACHINE LEARNING FOR AFFECT RECOGNITION AND INTERVENTION - Doctoral Dissertation","Stress recognition and monitoring from wearable sensor data is an emerging area of research with significant implications for an individual’s physical, social, and mental health. Mobile health interventions that incorporate real-time monitoring of physiological and behavioral stress markers enable tailored support during heightened-risk states. This dissertation develops a wearable-sensor framework for stress detection and real-time personalized intervention generation, including studies with individuals diagnosed with alcohol use disorder in rehabilitation settings. It also proposes and validates machine learning methods on real-world and in-lab datasets, studies links among stress, mood, and alcohol craving/consumption, and evaluates music listening as an intervention strategy for stress prevention and regulation.","WEARABLE SYSTEMS AND MACHINE LEARNING FOR AFFECT  \nRECOGNITION AND INTERVENTION  \nBy  \nRAMESH KUMAR SAH  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nWASHINGTON STATE UNIVERSITY School of Electrical Engineering and Computer Science  \nMAY 2024  \n© Copyright by RAMESH KUMAR SAH, 2024 All Rights Reserved  \n© Copyright by RAMESH KUMAR SAH, 2024 All Rights Reserved  \nTo the Faculty of Washington State University:  \nThe members of the Committee appointed to examine the dissertation of RAMESH KUMAR SAH find it satisfactory and recommend that it be accepted.  \nDiane Cook, Ph.D., Co-Chair  \nHassan Ghasemzadeh, Ph.D., Co-Chair  \nMichael Cleveland, Ph.D.  \nGanapati Bhat, Ph.D.  \nACKNOWLEDGMENT  \nI thank my advisor, committee members, research collaborators, and study participants for their support and contributions to this work. My advisor, Dr. Hassan Ghasemzadeh, has been a consistent source of inspiration and encouragement in my life for the past 5 years. I have learned a lot about research and scholarship from him, and I am forever grateful to him. I also want to sincerely thank and express my gratitude to Dr. Michael Cleveland for including me in the alcohol relapse study. Our findings from the relapse study gave a concrete direction to my research and ultimately made this dissertation possible. The ADARP project was funded by the Alcohol and Drug Research Program of Washington State University and was also supported in part by funds provided for medical and biological research by the State of Washington Initiative Measure (\\#171) . Other projects discussed in this dissertation were supported in part by the National Science Foundation under grants IIS-1954372 and CNS- 1750679. Any opinions, findings, conclusions, or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the funding organization.  \nWEARABLE SYSTEMS AND MACHINE LEARNING FOR AFFECT  \nRECOGNITION AND INTERVENTION  \nAbstract  \nby Ramesh Kumar Sah, Ph.D.  \nWashington State University  \nMay 2024  \nChairs: Diane Cook and Hassan Ghasemzadeh  \nStress recognition and monitoring from wearable sensor data is an emerging area of research with significant implications for an individual’s physical, social, and mental health. Mobile health interventions that incorporate real-time monitoring of physiological and behavioral markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress. This thesis presents a framework for stress detection using wearable sensor systems to facilitate real-time personalized intervention generation for stress management and regulation. We envision such a system to be incredibly beneficial for general as well as vulnerable population groups such as individuals diagnosed with alcohol use disorder. In a real-world study with individuals diagnosed with alcohol use disorder and undergoing rehabilitation programs to remain abstinent from alcohol, we studied the associations between alcohol craving/consumption, negative/positive mood, and stress. In doing so, we have studied, proposed, and validated solutions for important problems in mobile health sensor systems, such as sensor channel selection, optimal segment length, and processing of noisy sensor data. Furthermore, we present machine learning algorithms capable of learning from raw sensor data without feature computation and selection for  \nstress detection and classification. We validated proposed machine learning algorithms on real-world and in-lab datasets. Finally, we examined the relationship between music listening and stress using data collected in a lab study. We also statistically established music listening as an intervention strategy for stress prevention and regulation. We lay the foundation of a framework for future research incorporating music listening for stress intervention in an intelligent mobile health s","cbCaivQwZCoNlpkS","https://ap.wps.com/l/cbCaivQwZCoNlpkS","pdf",7375310,1,167,"English","en",105,"# Acknowledgment\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 Introduction\n# Chapter 2 Background and Related Work\n## 2.1 Stress and Physiology\n## 2.2 Machine Learning\n## 2.3 Alcohol Addiction and Stress\n## 2.4 Music and Stress\n# Chapter 3 Stress and Alcohol Addiction\n## 3.1 Study Protocol\n## 3.2 Sensor System\n## 3.3 Ecological Momentary Assessment (EMA) & Interviews\n## 3.4 Collected Data\n## 3.5 Statistical Analysis\n## 3.6 Conclusion\n# Chapter 4 Signal Quality and Outlier Removal","[{\"question\":\"What is the main goal of the wearable-system framework presented in the dissertation?\",\"answer\":\"To detect stress from wearable sensor data and generate real-time personalized interventions that support stress management and regulation.\"},{\"question\":\"How does the dissertation connect stress measurement with alcohol use disorder?\",\"answer\":\"It examines associations among alcohol craving/consumption, negative/positive mood, and stress using data collected from individuals diagnosed with alcohol use disorder in rehabilitation programs.\"},{\"question\":\"What role does machine learning play in the proposed approach?\",\"answer\":\"Machine learning algorithms are trained to learn from raw sensor data without feature computation or selection for stress detection and classification, validated on real-world and in-lab datasets.\"}]","WEARABLE SYSTEMS AND MACHINE LEARNING FOR AFFECT RECOGNITION AND INTERVENTION - 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