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.

Fruet D., Bara C., Pernice R., Faes L., Nollo G. (2022). Assessment Of Driving Stress Through SVM And KNN Classifiers On Multi-Domain Physiological Data. In Proceedings of 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON 2022) (pp. 920-925) [10.1109/MELECON53508.2022.9842891].

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

Pernice R.;Faes L.;
2022-06

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.
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
978-1-6654-4280-0
https://ieeexplore.ieee.org/document/9842891
Fruet D., Bara C., Pernice R., Faes L., Nollo G. (2022). Assessment Of Driving Stress Through SVM And KNN Classifiers On Multi-Domain Physiological Data. In Proceedings of 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON 2022) (pp. 920-925) [10.1109/MELECON53508.2022.9842891].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/567643
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