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.

Library of Congress Subject Headings

Human-machine systems--Health aspects; Mental health services--Technological innovations; User interfaces (Computer systems)--Design

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

Comments

This dissertation has been embargoed. The full-text will be available on or around 6/27/2025.

Campus

RIT – Main Campus

Plan Codes

ENGR-PHD

Download

Available for download on Friday, June 27, 2025

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