Encoding Methods Comparison for Stress Detection

Stress is a prevalent and growing phenomenon in the modern world that could lead to significant physical issues, both physical and mental health. Analyzing physiological signals collected from wearable sensors using artificial intelligence methods has emerged as a promising approach to predicting and managing stress. However, conventional models for time series analysis are RNN architectures and encounter challenges like high computational costs and issues with vanishing or exploding gradients. Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into images by applying encoding time series algorithms. This work intends to compare three time-series encoding methods: Gramian Angular Field (GAF), both summation and difference, Markovian Transition Field (MTF) and Recurrent Plot (RP) in the stress detection scenario. We employ two architectures, VGG-16 and ResNet, based on Convolutional Neural Network (CNN), to evaluate the performance of these methods on a public dataset, WESAD. Our results demonstrate that the GAF encoding method proves to be the most effective for classifying physiological signals related to stress.