Machine Learning for Predicting Personality and Psychological Symptoms from Behavioral Dynamics

Recent studies have explored correlations between behavioral expressions (facial features, body activity intensities) and personality traits or psychological symptoms, yet systematic research remains limited. Addressing this gap is critical, as understanding these relationships could transform mental health assessment by enabling non-invasive, real-time psychological monitoring. We assessed 167 participants using the SCL-90 and BFI-2 scales. Facial features were extracted via OpenFace 2.0, and body activity intensities via YOLOv7-Pose. Correlation analyses revealed significant links between behavioral expressions and psychological characteristics. Machine learning models (MLP, XGB, GRU) were developed using 56 behavioral features, with performance evaluated by f1-score, precision, recall, and AUC. Key findings showed a negative correlation between phobic anxiety and AU07, AU15, and Gaze-angle-x activity (p < 0.01), and a positive correlation between conscientiousness and AU07, Pose-Rz, and neck activity (p < 0.01). The bimodal GRU model, integrating all features, achieved the highest predictive performance (AUC = 0.736, precision = 0.714, recall = 0.714, f1-score = 0.71). This study highlights the feasibility of using machine learning to predict personality traits and psychological symptoms from facial and body activity data. The results affirm specific associations between behavioral expressions and psychological traits, providing a foundation for precise, personalized mental health assessment tools. This study introduces a novel integration of facial and body data using machine learning, revealing new behavioral–psychological associations and establishing an innovative framework for automated, personalized mental health assessment.