Abstract
About one in four American adults (26%) and 20.3% of children aged 8 to 16 years reported behavioral health concerns in 2023. However, a substantial gap remains between demand for behavioral healthcare services and access to such services. In response, research efforts have been made to advance effective and accessible approaches to provide assessments and treatments for populations with behavioral health needs. Yet, two primary challenges exist in current practice and research: 1) self-reports and manual observation-based assessments are subjective and labor-intensive, as evaluations are limited to the availability of clinical professionals; 2) they only target the general experiences but cannot capture specific details, such as the exact time when particular behavioral health concerns occurred and have minimal capability for continuous monitoring of an individual’s behavioral patterns. Therefore, this dissertation work developed human-machine interaction technologies to discover objective behavioral patterns through automated assessment of continuous signals (e.g., eye gaze). Three interaction modes – human-human, human-machine, and hybrid human-machine – were studied across three populations (intimate couples with moderate alcohol consumption, children with autism, and the primary caregivers of autistic children) to illustrate important design aspects and the preliminary efficacy. Novel multimodal measurements based on non-verbal behaviors, physiological signals, and interaction dynamics were proposed to capture unique behavioral cues that were important to the targeted populations. New data analysis pipelines were introduced, incorporating behavioral response quantification, signal processing, machine learning, and statistical modeling, for systematic evaluations. This dissertation work demonstrated the potential of using human-machine interaction as an effective tool for behavioral health assessments.
Publication Date
5-24-2024
Document Type
Dissertation
Student Type
Graduate
Degree Name
Engineering (Ph.D.)
Department, Program, or Center
Biomedical Engineering
College
Kate Gleason College of Engineering
Advisor
Zhi Zheng
Advisor/Committee Member
Cory A. Crane
Advisor/Committee Member
Dan Phillips
Recommended Citation
Yu, Zhiwei, "Human-Machine Interactions for Behavioral Health Care" (2024). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11789
Campus
RIT – Main Campus
Comments
This dissertation has been embargoed. The full-text will be available on or around 6/27/2025.