Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data

Abstract

Healthcare professionals, particularly nurses, face elevated occupational stress, a concern amplified during the COVID-19 pandemic. While wearable sensors offer promising avenues for real-time stress monitoring, existing studies often lack comprehensive datasets and robust analytical frameworks. This study addresses these gaps by introducing a multimodal dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature. A systematic literature review identified limitations in prior stress-detection methodologies, particularly in handling class imbalance and optimizing model generalizability. To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE), ensuring balanced representation of stress states. Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking Classifier to leverage their collective predictive strengths. By using a publicly accessible dataset and a reproducible analytical pipeline, this work advances the development of deployable stress-monitoring systems, offering practical implications for safeguarding healthcare workers' mental health. Future research directions include expanding demographic diversity and exploring edge-computing implementations for low latency stress alerts.