Assessment Of Driving Stress Through SVM And KNN Classifiers On Multi-Domain Physiological Data

Abstract

We propose an objective stress assessment method based on the extraction of features from physiological time series and their classification using Support Vector Machine and K-Nearest Neighbors algorithms. For this purpose, we used an open dataset consisting of multiparametric physiological signals (electrocardiogram, electromyogram, galvanic skin response and breath signal) obtained during the execution of a driving route within the city of Boston with restful, highway and city driving periods indicative of three different stress states. To predict the driver stress level, 21 features were extracted from 122 chunks of raw signals and were subsequently managed by classification algorithms. Our analysis showed a prediction accuracy of 98.4% when all features were used, decreasing when signals from specific physiological systems were not considered. Our results highlighted that multidomain data acquisition by wearable sensors combined with appropriate classification models may represent a promising strategy to detect drivers' stress status in an unobtrusive and objective way that can in perspective be applicable in several other fields such as in the clinics.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

“…In this work, to assess variations among different physiological conditions, we evaluated the mean values of the GSR level and SCL component computed on the whole durations of each phase of the experimental protocol (R1, SF, R2, BH, R3) [ 52 ]. Finally, the number of peaks of the SCR component was also computed considering 10-s windows before and after each phase transition [ 52 ]. This choice considered the number of generated peaks in relation to the level of cognitive and emotional stress [ 53 ].…”

Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function

“…Based on physiological data (i.e., eye movement and heart rate) and environmental information such as vehicle speed, they evaluated whether lane changing was risky or safe. Fruet et al [6] focused on stress as a driving risk and predicted the stress levels from physiological signals including galvanic skin responses, electrocardiograms, electromyograms, and breathing signals. These studies succeeded in utilizing physiological data during driving by specifying the situation and limiting the estimation target to the driver's state rather than crashes.…”