Comparison of Machine Learning approaches for Stress Detection from Wearable Sensors Data

Stress is a prevalent and growing phenomenon in the modern world potentially leading to significant repercussions on both physical and mental health. The analysis of physiological signals, collected from wearable sensors, has emerged as a promising approach to predicting and managing stress. Methods based on machine learning techniques have been defined in the literature and achieved promising results by using handcrafted features extracted from the signal. However, there is no consensus on the list of features, while deep learning approaches that overcomes the problem require significant computational power and a large amount of data. In this paper, we present a comprehensive view of the most common representative machine learning algorithms applied to the stress detection domain by giving a reference point for both academia and industry professionals in this application field. This study considers fragments of signals without extracting any features and uses a public dataset, WESAD, that contains high-resolution physiological, including blood volume pulse, electrocardiogram and electromyogram. The data collected from 15 subjects during a lab study are heterogeneous and characterized by different frequencies and noises due to some devices. After preprocessing, we assess the performance of ten machine learning algorithms belonging to four models (tree, ensemble, linear and neighbours) on the WESAD by facing the problem as binary (stress/no-stress) and multiclass (baseline, stress, and amusement) classifications. Our results, evaluated in terms of classical metrics, show that Random Forest outperforms the others in binary and multi-class approaches.