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Inspired by the human ability to infer emotions from body language, wepropose an automated framework for body language based emotion recognitionstarting from regular RGB videos. In collaboration with psychologists, wefurther extend the framework for psychiatric symptom prediction. Because aspecific application domain of the proposed framework may only supply a limitedamount of data, the framework is designed to work on a small training set andpossess a good transferability. The proposed system in the first stagegenerates sequences of body language predictions based on human poses estimatedfrom input videos. In the second stage, the predicted sequences are fed into atemporal network for emotion interpretation and psychiatric symptom prediction.We first validate the accuracy and transferability of the proposed bodylanguage recognition method on several public action recognition datasets. Wethen evaluate the framework on a proposed URMC dataset, which consists ofconversations between a standardized patient and a behavioral healthprofessional, along with expert annotations of body language, emotions, andpotential psychiatric symptoms. The proposed framework outperforms othermethods on the URMC dataset.

Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation