Stress detection with encoding physiological signals and convolutional neural network

Stress is a significant and growing phenomenon in the modern world that leads to numerous health problems. Robust and non-invasive method developments for early and accurate stress detection are crucial in enhancing people's quality of life. Previous researches show that using machine learning approaches on physiological signals is a reliable stress predictor by achieving significant results. However, it requires determining features by hand. Such a selection is a challenge in this context since stress determines nonspecific human responses. This work overcomes such limitations by considering STREDWES, an approach for Stress Detection from Wearable Sensors Data. STREDWES encodes signal fragments of physiological signals into images and classifies them by a Convolutional Neural Network (CNN). This study aims to study several encoding methods, including the Gramian Angular Summation/Difference Field method and Markov Transition Field, to evaluate the best way to encode signals into images in this domain. Such a study is performed on the NEURO dataset. Moreover, we investigate the usefulness of STREDWES in real scenarios by considering the SWELL dataset and a personalized approach. Finally, we compare the proposed approach with its competitors by considering the WESAD dataset. It outperforms the others.