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Extracting Multimodal Embeddings via Supervised Contrastive Learning for Psychological Screening

The diagnosis of psychological disorders in early childhood is of utmost importance given their severe impact on children's academic and social skills as well as general adaptive functioning. Wearable and video-based systems have the potential to collect important diagnostic information in the form of neurophysiological and behavioral signals. However, accurate prediction of psychological disorder status from multimodal data streams necessitates their combination into meaningful features for classification models. In this work, we present a multitask supervised contrastive learning approach to learn useful multimodal embeddings from functional Near-Infrared Spectroscopy, galvanic skin response, and facial video data collected during a frustration-inducing task. The generated embeddings are able to accurately infer emotion regulation-related psychological disorders with an F1 score of 0.91, having significant implications for early-childhood mental health diagnoses.