High-Resolution Physiological Stress Prediction Models based on Ensemble Learning and Recurrent Neural Networks

Abstract

High-resolution stress detection is an essential requirement for designing time- and event-based stress monitoring systems as a building block for mobile and e-health systems aimed at supporting personalised treatments, both in clinical and remote settings. However, most of the existing solutions focus on binary or few-class stress detection, thus providing a limited feedback and reducing their utility and applicability in real- world scenarios. In this paper we present an alternative approach that overcomes the standard formulation of stress detection as supervised classification problem, by using ensemble learners and recurrent neural networks (RNNs) as the most relevant models for solving time series regression tasks. We trained and tested models using WESAD, a public multimodal wearable dataset for stress and affect detection, and we defined and computed stress scores based on various validated questionnaires stored in the dataset to be used as ground truth. Leave-One- Subject-Out (LOSO) cross-validation scheme has been applied to test the generalisation capabilities of each model in predicting individual stress scores. Results show that Nonlinear AutoRegressive network with eXogenous inputs (NARX), Random Forest (RF), and Least-Squares Gradient Boosting (LSBoost) provide high-resolution personalised stress predictions for the majority of analysed subjects. The proposed predictive models may be integrated as support to decision making into a Decision Support System (DSS) for online stress monitoring, with the main goal to design personalised stress management and alleviation strategies related to the inferred stress severity.