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

Book excerpt: This PhD 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. Full content at: https://doi.org/10.26180/19728097.v1