High-Accuracy Stress Detection Using Wrist-Worn PPG Sensors

Stress has become a prevalent issue affecting individuals’ physical and mental well-being. Detecting stress is the first crucial step to managing it and preventing it from causing other health issues. In this paper, we present a new method to improve the performance of detecting stress, using a comfortable to wear sensor, namely Photoplethysmography (PPG), which is embedded virtually in all smartwatches. To this end, we use PPG sensor data from the publicly available wearable stress and affect detection dataset (WESAD). Using new denoising processes, segmentation methods, and key feature extract, we achieve 95.55% accuracy in detecting stress using the Support Vector Machine (SVM) algorithm. Simplifying the process alongside improved accuracy in this paper facilitates smartphone usage as a real-time stress detection, which we plan as future work.