Machine learning model for stress prediction

Abstract

Modern advancements in wearable sensor technology have enabled the ease of non- intrusive monitoring of physiological biomarkers like Electrodermal Activity (EDA) and Heart Rate (HR) that could be directly linked to elevated stress levels. While past research has been done in this domain, specifically in using machine learning to detect stress levels, the endeavour of developing a machine learning model capa- ble of generalising well on new, unseen data still remains. Stress responses can be reflected by biological and psychological components that vary across unique individ- uals, exacerbating the complications of developing a generic stress detection model. One of the most significant challenges is the lack of large, publicly available datasets labeled for stress prediction that can be used to develop robust machine learning mod- els. In this project, we present a stacking ensemble model combining XGBoost and Artificial Neural Networks (ANN) to predict stress levels using wearable device data, and then evaluate their generalisation ability for predictions on new, unseen data. By demonstrating the effectiveness of stacking ensembles in improving stress prediction and highlighting the need for addressing class imbalance to enhance model sensitivity, this research contributes to the development of robust stress monitoring systems for health applications and provides insights for the further study of stress detection using physiological bio-signals recorded using wearable technologies.