Stress Detection Using Multimodal Physiological Signals With Machine Learning From Wearable Devices

Pranita Subhash Shedage, Seyedamin Pouriyeh, Reza M. Parizi, Meng Han, Giovanna Sannino, Nasrin Dehbozorgi

Abstract: Stress is considered one of the most prevalent concerns among individuals. Studies have shown that experiencing long-term stress can cause severe health issues such as cardiovascular diseases, hypertension, depression, etc. Preventative measures, such as early stress detection, can help individuals mitigate these health issues. When a person gets stressed, physiological values like blood volume pulse, temperature, and electrodermal activity signals get affected. Machine Learning techniques can be utilized to identify stress by analyzing these physiological signals. This paper presents a machine learning method for detecting stress levels of an individual using the publicly available dataset called "Wearable Stress and Affect Detection"(WESAD), which has physiological data collected from the wrist-worn and chest-worn sensors attached to 15 different subjects. We used physiological signals, including Blood Volume Pulse(BVP), Body Temperature(TEMP), and Electrodermal Activity(EDA) signals, collected from wrist-worn sensors to detect the state of the mind. For the implementation, we used different Machine Learning models, like Logistic Regression, Decision Tree, Random Forest, and Stacking Ensemble Learning technique. During the investigation, personalized models, utilizing individual subject data, and generalized models, amalgamating all subject data, were developed. Evaluation reveals accuracy values reaching up to 99% and 91% for individual subject data and combined data, respectively.