Machine Learning-Based Mental Health Classification Using Physiological and Physical Activity Data from Wearable Sensors

Abstract

Mental health profoundly impacts individuals’ quality of life, productivity, and holistic well-being. The early identification of mental health disorders remains problematic, owing largely to the dependence on subjective evaluation methods. This study addresses these limitations by establishing a machine-learning-based mental health classification framework that leverages physiological and physical activity metrics from wearable sensors. Key physiological features, including heart rate, heart rate variability (HRV), sleep quality, and stress levels, along with physical activity data, were systematically collected and preprocessed via cleaning, normalization, and encoding. The performance of three distinct machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), was evaluated using accuracy, precision, recall, and F1 score. Empirical results revealed that the Random Forest model attained the highest classification performance, with accuracy, precision, recall, and F1-score values of 96.34%, 96.37%, 96.34%, and 96.34%, respectively, outperforming both SVM and KNN models. The findings underscore the utility of multimodal physiological and behavioral data from wearable devices as objective markers for mental health status. By integrating physiological indicators, activity patterns, and psychological assessments into a cohesive machine learning architecture, this research advance’s objective, continuous mental health monitoring.