Sensing and Interventions for Social Anxiety
A Review
Vesna Poprcova (TU Delft - Technology, Policy and Management)
Iulia Lefter (TU Delft - Technology, Policy and Management)
Martijn Warnier (TU Delft - Technology, Policy and Management)
Frances Brazier (TU Delft - Technology, Policy and Management)
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Abstract
In recent years, there has been increasing interest in leveraging behavioural and physiological data to detect Social Anxiety (SA), with multimodal sensing and machine learning techniques showing significant promise. Concurrently, a growing body of research explores technology-assisted psychological interventions designed to improve treatment outcomes by addressing limitations in traditional therapies. These interventions aim to adapt to individual differences in anxiety levels, symptom severity, and user preferences. This review presents a comprehensive overview of technical approaches to supporting individuals with SA, with a dual focus on advancements in sensing and intervention technologies. It synthesises emerging trends across disciplines, highlights existing research gaps, and proposes directions for future work at the intersection of artificial intelligence, human-centred sensing, and mental health support.