Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Abstract

Stress can adversely impact health, leading to issues like high blood pressure, heart diseases, and a compromised immune system. Consequently, using wearable devices to monitor stress is essential for prompt intervention and effective management. This study investigates the efficacy of wearable devices in the early detection of psychological stress, employing both binary and five-class classification models. Significant correlations were observed between stress levels and physiological signals, including Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP), establishing these modalities as reliable biomarkers for stress detection. Utilizing the publicly available Wearable Stress and Affect Detection (WESAD) dataset, we employed two ensemble methods, Majority Voting (MV) and Weighted Averaging (WA), to integrate these signals, achieving maximum accuracies of 99.96% for binary classification and 99.59% for five-class classification. This integration significantly enhances the accuracy and robustness of the stress detection system. Furthermore, ten different classifiers were evaluated, and hyperparameter optimization and K-fold cross-validation ranging from 3-fold to 10-fold were applied. Both time-domain and frequency-domain features were examined separately. A review of commercially available wearable devices supporting these modalities was also conducted, resulting in recommendations for optimal configurations for practical applications. Our findings highlight the potential of multimodal wearable devices in advancing the early detection and continuous monitoring of psychological stress, with significant implications for future research and the development of improved stress detection systems.