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Multimodal-multisensor Analytics for Detecting Anxiety Phases in Individuals Experiencing High Anxiety

thesis
posted on 08.05.2022, 06:00 by HASHINI HIRANYA SENARATNE
This thesis aims to advance objective assessments of anxiety to address the drawbacks of current clinical assessments. It uses multiple methods, including semi-structured interviews, lab-based data collection, signal analysis techniques, and multimodal-multisensor analytics. In total, 147 subjects participated in qualitative and quantitative data collection studies. Its results detected high-anxious vs. low-anxious individuals, conceptualized four anxiety phases, and detected all those phases in 65% of high-anxious individuals by fusing three physiological and behavioral features; a 30% improvement compared to the best unimodal feature. Overall, this thesis is a fundamental contribution toward the long-term aims of minimizing the burden of anxiety disorders.

History

Campus location

Australia

Principal supervisor

Kirsten Ellis

Additional supervisor 1

Sharon Oviatt

Additional supervisor 2

Glenn Melvin

Additional supervisor 3

Levin Kuhlmann

Year of Award

2022

Department, School or Centre

Human Centred Computing

Course

Doctor of Philosophy

Degree Type

DOCTORATE