Empowering vision machine perception for robust telehealth applications

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Hoang, Trung Hieu

Contributors dc:contributor

• Do, Minh N
• Cunningham, Brian T
• Hsiao-Wecksler, Elizabeth T
• Shomorony, Ilan
• Wang, Yuxiong

• AI for healthcare, computer vision, machine learning, human-motion analysis, biosensors, test-time adaptation

Statement dc:rights

• Copyright 2025 Trung Hieu Hoang

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132490

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132490