Exploring the Potential of Affective Computing for Social Anxiety: A Review of Sensing and Interventions

Over the past decade, there has been growing interest in using human behavioral and physiological data to detect Social Anxiety Disorder (SAD). Machine learning and deep learning techniques that use multimodal sensing have emerged as promising tools for detecting SAD characteristics. Additionally, extensive research on technology-assisted psychological interventions for SAD aims to enhance treatment efficacy and address the shortcomings of existing treatments by exploring how these interventions can be tailored to individual anxiety levels, symptom severity, and personal preferences. This review provides an overview of approaches for generalised SAD, covering advancements in both sensing and interventions while highlighting the potential of affective computing. It synthesises key insights on current emerging trends, identifies research gaps, and outlines directions for future research.