EOSAD: An Event-Oriented Physiological and Behavioral Social Anxiety Annotation Dataset in Virtual Reality

Qinyao Hou, Ding Ding, Jiaju Yang, Jiahang Tu

Abstract: Social phobia is a prevalent mental health con-dition characterized by overwhelming fear on apprehension of social situations, often leading individuals to avoid such encounters. It is a very common mental disorder among adolescents and can have a significant bad effect on their social skills and well-beings if not intervened early. Existing methods to assess social anxiety level rely on self-report scales or clinical diagnosis, both of which may face challenges like subjective judgments and patient resistance. To address this, we applied Virtual Reality technology (VR) and introduced event-oriented social anxiety dataset (EOSAD) to serve for fine-grained social anxiety level detected model. A case study (N = 44) was conducted where participants wore a Vive Pro Eye HMD to experience two types of scenes and labeled their social anxiety level accordingly. During the experiment, relevant signals and social anxiety scores were collected, including (1) behavioral signals (head and eye movements) (2) physiological signals (heart rate (HR), electrodermal activity (EDA), etc.) (3) discrete social anxiety self-reported labels (4) evaluation questionnaires. We first verified the labels, and furthermore ran baseline classification experiments, where GRU model showed best accuracy: 92.84 %for binary classification. It was also found that either behavioral data or physiological signals alone could achieve satisfactory accuracies (85.64% and 91.02%) while the combined achieved slightly higher(92.84 %)