A Multi-User Multi-Task Model For Stress Monitoring From Wearable Sensors

This study presents results on stress monitoring based on peripheral physiological signals acquired from wearable sensors. Using machine learning methods on data collected during laboratory experiment on 6 subjects exposed to 3 kinds of well-known stress tasks (TSST, MST and SECPT), the influence of the inter-subject and the inter-task variability on stress/no stress classification is evaluated. The relevant set of features and the type of classifier (choice between SVM, Decision Tree, and Naïve Bayes) are first selected by using Leave-One-Subject-Out (LOSO) cross validation. The model learnt on the 3 tasks reach 85% of good classification rate over the validation set, using SVMs as classifier and as features: the mean of the heart rate and the sum of all skin response's width-prominence products. After that, different results obtained using several partitioning of learning/validation sets are compared (e.g. learning on one task and on all subjects and validating on the other tasks) in order to evaluate the subjects and tasks influence. We observe that our model is robust to the inter-subject and the inter-task variability with small differences for SECPT which suggests a slightly different physiological reaction to physical stressors.