A hybrid LSTM-XGBoost model using wearable physiological sensor data accurately identifies mental health states such as stress, mood, and fatigue in college students.
ABSTRACT This research has introduced a hybrid model that integrates the long short‐term memory (LSTM) and extreme gradient boosting (XGBoost) models to assess students' mental health states, particularly to identify students' levels of stress, mood, and fatigue. The physiological measures measured were heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and skin temperature. All measures were recorded using wearable sensors and underwent processing, such as normalization, noise filtering, and feature extraction, to ensure the signal quality was fit for analysis and interpretability. While the LSTM network can accurately represent the temporal dynamics present in the physiological sequences, the XGBoost model is critical in obtaining high accuracy through the classification of features' non‐linear interactions and decision boundary optimization. The experimental validation through the technique of fivefold cross‐validation shows that the hybrid model performs with high accuracy of 0.98 on average, F1‐score of 0.98, and consistently low false‐positive and false‐negative rates when compared to SVM, Random Forest, and single deep learning model methods that serve as baseline methods. The results assure the framework's reliability, consistency, and clarity in reasoning over different data conditions. This novel method provides a strong platform for the real‐time, data‐driven monitoring and early detection of psychological distress, thus allowing educators, mental‐health professionals, and caregivers to make timely interventions and improve the overall well‐being of students.
Shang et al. (2026) studied this question.