TEANet: A Transpose-Enhanced Autoencoder Network for Wearable Stress Monitoring
Mental stress poses a significant public health concern due to its detrimental effects on physical and mental well-being, necessitating the development of continuous stress monitoring tools for wearable devices. Blood volume pulse (BVP) sensors, readily available in many smartwatches, offer a convenient and cost-effective solution for stress monitoring. This study presents a deep learning approach, a Transpose-Enhanced Autoencoder Network (TEANet), for stress detection using BVP signals on resource-constrained devices. The proposed TEANet model was trained and validated utilizing a self-developed RUET SPML dataset, and the publicly available wearable stress and affect detection (WESAD) dataset. It achieves the highest accuracy of 92.94% and 96.94%, F1 scores of 95.16% and 95.95%, and kappa of 0.8181 and 0.9350 for RUET SPML, and WESAD datasets, respectively. The proposed TEANet effectively detects mental stress through BVP signals with high accuracy, making it a promising tool for continuous stress monitoring. Furthermore, deploying the proposed model on the Raspberry Pi 3B+ enhances its potential for reliable real-time stress monitoring using resource-constrained devices.