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.

The aim of this research is to design an adaptive interactive system with the capability to sense the current states of affective behaviour and provide adaptive interventions to support users navigating social anxiety. Systems that adapt to anxiety levels, symptom severity, or user preferences can potentially better support them and increase adherence. Sensing users' current affective states and tailoring interventions accordingly will be explored in order to empower users to manage their social anxiety and ultimately enhance their overall well-being, given that human emotions and emotion regulation are critical factors in social anxiety. In doing so, an interdisciplinary approach will be followed, integrating knowledge from design, psychology, affective computing, and human-computer interaction and drawing upon state-of-the-art technological advancements.

The ability to reason by analogy is essential for many cognitive processes from low-level and high-level perception to categorization. Intuitively, the idea is to use what is already known to explain new observations that appear similar to old knowledge. In a sense, it is opposite of induction, where to explain the observations one comes up with new hypotheses/theories. Therefore, a system capable of both types of reasoning would be superior. In this paper, the authors present an overview of Inductive Logic Programming (ILP) systems that use reasoning by analogy and discuss the results of combining Analogical Prediction with an ILP system, showing that, for some cases, it is possible to improve significantly the learning speed of the ILP system. This paper will examine the problems that arise in the context of a physically embodied robot that tries to learn regularities in its environment.