{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125506"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125506","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning approaches in practical anxiety detection","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Alkurdi, Abdul E."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hsiao-Wecksler, Elizabeth T","Hernandez, Manuel E","Shao, Chenhui","Sowers , Richard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-08","date_published":"2024-07-08","updated_at":"2026-07-22T22:25:02Z","subjects":["Affective Computing","Anxiety Detection","Wearable Technology","Ai For Health","Applied Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Abdulrahman Alkurdi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125506","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hsiao-Wecksler, Elizabeth T","Hernandez, Manuel E","Shao, Chenhui","Sowers , Richard"]},{"key":"dc:creator","label":"Author","values":["Alkurdi, Abdul E."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-08","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Affective Computing","Anxiety Detection","Wearable Technology","Ai For Health","Applied Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Abdulrahman Alkurdi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125506"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Abdul Alkurdi, accepted the attached license on 2024-05-14 at 11:32.","The student, Abdul Alkurdi, submitted this Dissertation for approval on 2024-05-14 at 13:47.","This Dissertation was approved for publication on 2024-07-08 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20798 on 2025-02-04 at 21:03:19","This dissertation investigates the development and application of machine learning (ML) techniques for the robust detection of anxiety using wearable technology under diverse environmental conditions. The research encompasses three interlinked studies, each contributing uniquely towards advancing anxiety detection technologies in real-world settings. The first study conducts a thorough review of existing ML methodologies for anxiety detection, identifying the evolution from traditional feature-based (FB) models to advanced end-to-end (E2E) deep learning approaches. It evaluates their applicability across different scenarios, highlighting the challenges of integrating such technologies in practical applications due to issues like noise interference and model overfitting. In the second study, the focus shifts to the practical implementation of these models in noisy environments. It explores the resilience of both FB and E2E models by introducing artificial noise into the WESAD dataset and examining their performance. This part of the research emphasizes the critical impact of environmental disturbances on model accuracy, particularly in wearable technologies, and demonstrates the superior noise resistance of FB models compared to E2E models. The third study extends this analysis by applying transfer learning techniques to adapt these models to real-world datasets, specifically the RADWear and WEAR datasets, which reflect a broad spectrum of real-life conditions. The study assesses the efficacy of transfer learning in enhancing model robustness and addresses the challenges of deploying these technologies in dynamic and uncontrolled environments. Despite the high potential of transfer learning, the results reveal that E2E models consistently underperform in comparison to FB models, which display greater adaptability and reliability under varied environmental conditions. Overall, this dissertation highlights the complexities of developing effective ML-based anxiety detection systems that are capable of operating in real-world scenarios. It underscores the need for further research into optimizing model architectures, improving noise management strategies, and refining data collection techniques to enhance the practicality and effectiveness of anxiety detection tools. The insights gained from these studies pave the way for future advancements in wearable technology for mental health, offering a foundation for more personalized and responsive mental health care solutions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning approaches in practical anxiety detection"]}]}],"canonical_facts":{"dc:contributor":["Hsiao-Wecksler, Elizabeth T","Hernandez, Manuel E","Shao, Chenhui","Sowers , Richard"],"dc:creator":["Alkurdi, Abdul E."],"dc:date":["2024-07-08","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Abdul Alkurdi, accepted the attached license on 2024-05-14 at 11:32.","The student, Abdul Alkurdi, submitted this Dissertation for approval on 2024-05-14 at 13:47.","This Dissertation was approved for publication on 2024-07-08 at 10:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20798 on 2025-02-04 at 21:03:19","This dissertation investigates the development and application of machine learning (ML) techniques for the robust detection of anxiety using wearable technology under diverse environmental conditions. The research encompasses three interlinked studies, each contributing uniquely towards advancing anxiety detection technologies in real-world settings. The first study conducts a thorough review of existing ML methodologies for anxiety detection, identifying the evolution from traditional feature-based (FB) models to advanced end-to-end (E2E) deep learning approaches. It evaluates their applicability across different scenarios, highlighting the challenges of integrating such technologies in practical applications due to issues like noise interference and model overfitting. In the second study, the focus shifts to the practical implementation of these models in noisy environments. It explores the resilience of both FB and E2E models by introducing artificial noise into the WESAD dataset and examining their performance. This part of the research emphasizes the critical impact of environmental disturbances on model accuracy, particularly in wearable technologies, and demonstrates the superior noise resistance of FB models compared to E2E models. The third study extends this analysis by applying transfer learning techniques to adapt these models to real-world datasets, specifically the RADWear and WEAR datasets, which reflect a broad spectrum of real-life conditions. The study assesses the efficacy of transfer learning in enhancing model robustness and addresses the challenges of deploying these technologies in dynamic and uncontrolled environments. Despite the high potential of transfer learning, the results reveal that E2E models consistently underperform in comparison to FB models, which display greater adaptability and reliability under varied environmental conditions. Overall, this dissertation highlights the complexities of developing effective ML-based anxiety detection systems that are capable of operating in real-world scenarios. It underscores the need for further research into optimizing model architectures, improving noise management strategies, and refining data collection techniques to enhance the practicality and effectiveness of anxiety detection tools. The insights gained from these studies pave the way for future advancements in wearable technology for mental health, offering a foundation for more personalized and responsive mental health care solutions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125506"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Abdulrahman Alkurdi"],"dc:subject":["Affective Computing","Anxiety Detection","Wearable Technology","Ai For Health","Applied Machine Learning"],"dc:title":["Machine learning approaches in practical anxiety detection"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}