{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132490"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132490","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Empowering vision machine perception for robust telehealth applications","abstract":"Today, the healthcare system faces significant challenges driven by a shortage of health workers, the growing demand for personalized care, and an aging population. This creates a crisis of bottlenecks in symptom triage and longitudinal monitoring. Digital telehealth toolbox and AI-assisted diagnosis systems offer promising solutions as force multipliers to alleviate these challenges. Among them, smartphone vision-based telehealth exams have gained significant interest due to their powerful computing power, serving as valuable point-of-care sensors. Yet, developing vision-based modules to extract digital biomarkers from visual data and reliably deploying them in real-world scenarios remains a key challenge. This dissertation concentrates on two primary research paths: empowering computer vision for telehealth and deploying robust machine learning (ML) models for telehealth applications. Under the first focus, the Digitized Neurological Examination (DNE) system is introduced for comprehensive vision-based neurological examination using smartphones, validated for clinical relevance and abnormality detection and documentation. Additionally, the smartphone-based viral pathogen detection system, PathTracker, is presented for rapid point-of-care diagnosis through innovative image processing techniques. In the second focus, although test-time adaptation (TTA) techniques offer promise in handling domain-shift challenges during ML model deployment, they are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the introduction of “persistent TTA” and “reusing of incorrect prediction attack (RIP)” to ensure stability in dynamic testing environments. These contributions drive forward robust telehealth solutions for neurological care and viral pathogen detection, providing effective responses to future healthcare challenges.","abstract_html":"Today, the healthcare system faces significant challenges driven by a shortage of health workers, the growing demand for personalized care, and an aging population. This creates a crisis of bottlenecks in symptom triage and longitudinal monitoring. Digital telehealth toolbox and AI-assisted diagnosis systems offer promising solutions as force multipliers to alleviate these challenges. Among them, smartphone vision-based telehealth exams have gained significant interest due to their powerful computing power, serving as valuable point-of-care sensors. Yet, developing vision-based modules to extract digital biomarkers from visual data and reliably deploying them in real-world scenarios remains a key challenge. This dissertation concentrates on two primary research paths: empowering computer vision for telehealth and deploying robust machine learning (ML) models for telehealth applications. Under the first focus, the Digitized Neurological Examination (DNE) system is introduced for comprehensive vision-based neurological examination using smartphones, validated for clinical relevance and abnormality detection and documentation. Additionally, the smartphone-based viral pathogen detection system, PathTracker, is presented for rapid point-of-care diagnosis through innovative image processing techniques. In the second focus, although test-time adaptation (TTA) techniques offer promise in handling domain-shift challenges during ML model deployment, they are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the introduction of “persistent TTA” and “reusing of incorrect prediction attack (RIP)” to ensure stability in dynamic testing environments. These contributions drive forward robust telehealth solutions for neurological care and viral pathogen detection, providing effective responses to future healthcare challenges.","abstract_has_math":false,"creators":["Hoang, Trung Hieu"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh N","Cunningham, Brian T","Hsiao-Wecksler, Elizabeth T","Shomorony, Ilan","Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["AI for healthcare, computer vision, machine learning, human-motion analysis, biosensors, test-time adaptation"],"languages":["en"],"rights":["Copyright 2025 Trung Hieu Hoang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132490","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N","Cunningham, Brian T","Hsiao-Wecksler, Elizabeth T","Shomorony, Ilan","Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Hoang, Trung Hieu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-11-25"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI for healthcare, computer vision, machine learning, human-motion analysis, biosensors, test-time adaptation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Trung Hieu Hoang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132490"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Today, the healthcare system faces significant challenges driven by a shortage of health workers, the growing demand for personalized care, and an aging population. This creates a crisis of bottlenecks in symptom triage and longitudinal monitoring. Digital telehealth toolbox and AI-assisted diagnosis systems offer promising solutions as force multipliers to alleviate these challenges. Among them, smartphone vision-based telehealth exams have gained significant interest due to their powerful computing power, serving as valuable point-of-care sensors. Yet, developing vision-based modules to extract digital biomarkers from visual data and reliably deploying them in real-world scenarios remains a key challenge. This dissertation concentrates on two primary research paths: empowering computer vision for telehealth and deploying robust machine learning (ML) models for telehealth applications. Under the first focus, the Digitized Neurological Examination (DNE) system is introduced for comprehensive vision-based neurological examination using smartphones, validated for clinical relevance and abnormality detection and documentation. Additionally, the smartphone-based viral pathogen detection system, PathTracker, is presented for rapid point-of-care diagnosis through innovative image processing techniques. In the second focus, although test-time adaptation (TTA) techniques offer promise in handling domain-shift challenges during ML model deployment, they are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the introduction of “persistent TTA” and “reusing of incorrect prediction attack (RIP)” to ensure stability in dynamic testing environments. These contributions drive forward robust telehealth solutions for neurological care and viral pathogen detection, providing effective responses to future healthcare challenges.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Trung Hieu Hoang, accepted the attached license on 2025-11-21 at 21:49.","The student, Trung Hieu Hoang, submitted this Dissertation for approval on 2025-11-21 at 22:04.","This Dissertation was approved for publication on 2025-11-25 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22866 on 2026-02-19 at 18:24:38"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Empowering vision machine perception for robust telehealth applications"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh N","Cunningham, Brian T","Hsiao-Wecksler, Elizabeth T","Shomorony, Ilan","Wang, Yuxiong"],"dc:creator":["Hoang, Trung Hieu"],"dc:date":["2025-12","2025-11-25"],"dc:description":["Today, the healthcare system faces significant challenges driven by a shortage of health workers, the growing demand for personalized care, and an aging population. This creates a crisis of bottlenecks in symptom triage and longitudinal monitoring. Digital telehealth toolbox and AI-assisted diagnosis systems offer promising solutions as force multipliers to alleviate these challenges. Among them, smartphone vision-based telehealth exams have gained significant interest due to their powerful computing power, serving as valuable point-of-care sensors. Yet, developing vision-based modules to extract digital biomarkers from visual data and reliably deploying them in real-world scenarios remains a key challenge. This dissertation concentrates on two primary research paths: empowering computer vision for telehealth and deploying robust machine learning (ML) models for telehealth applications. Under the first focus, the Digitized Neurological Examination (DNE) system is introduced for comprehensive vision-based neurological examination using smartphones, validated for clinical relevance and abnormality detection and documentation. Additionally, the smartphone-based viral pathogen detection system, PathTracker, is presented for rapid point-of-care diagnosis through innovative image processing techniques. In the second focus, although test-time adaptation (TTA) techniques offer promise in handling domain-shift challenges during ML model deployment, they are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the introduction of “persistent TTA” and “reusing of incorrect prediction attack (RIP)” to ensure stability in dynamic testing environments. These contributions drive forward robust telehealth solutions for neurological care and viral pathogen detection, providing effective responses to future healthcare challenges.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Trung Hieu Hoang, accepted the attached license on 2025-11-21 at 21:49.","The student, Trung Hieu Hoang, submitted this Dissertation for approval on 2025-11-21 at 22:04.","This Dissertation was approved for publication on 2025-11-25 at 08:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22866 on 2026-02-19 at 18:24:38"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132490"],"dc:language":["en"],"dc:rights":["Copyright 2025 Trung Hieu Hoang"],"dc:subject":["AI for healthcare, computer vision, machine learning, human-motion analysis, biosensors, test-time adaptation"],"dc:title":["Empowering vision machine perception for robust telehealth applications"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}