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Observation is an essential tool for understanding and studying humanbehavior and mental states. However, coding human behavior is a time-consuming,expensive task, in which reliability can be difficult to achieve and bias is arisk. Machine learning (ML) methods offer ways to improve reliability, decreasecost, and scale up behavioral coding for application in clinical and researchsettings. Here, we use computer vision to derive behavioral codes or conceptsof a gold standard behavioral rating system, offering familiar interpretationfor mental health professionals. Features were extracted from videos ofclinical diagnostic interviews of children and adolescents with and withoutobsessive-compulsive disorder. Our computationally-derived ratings werecomparable to human expert ratings for negative emotions,activity-level/arousal and anxiety. For the attention and positive affectconcepts, our ML ratings performed reasonably. However, results for gaze andvocalization indicate a need for improved data quality or additional datamodalities.

Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry