Work

Machine Learning for Predicting Personality and Psychological Symptoms from Behavioral Dynamics

Year: 2025

Type: article

Abstract: 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.

Source: Electronics

DOI: 10.3390/electronics14030583

Authors: Min Wang, Ao Xu, Chenxiao Fan, Xiao Sun

Institutions: Anhui Medical University, Hefei University of Technology, University of Science and Technology of China

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896