Driver stress level detection using HRV analysis

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dc.contributor.author Munla, Nermine
dc.contributor.author Khalil, Mohamad
dc.contributor.author Shahin, Ahmad
dc.contributor.author Mourad, Azzam
dc.date.accessioned 2017-03-09T13:35:05Z
dc.date.available 2017-03-09T13:35:05Z
dc.date.issued 2017-03-09
dc.identifier.isbn 9781467365161 en_US
dc.identifier.uri http://hdl.handle.net/10725/5346
dc.description.abstract This paper intends to investigate stress level detection of a driver during real world driving experiment. This detection is based on heart rate variability (HRV) analysis which is derived from ECG signal and reflects autonomic nervous system state of the human body. The alteration of autonomic nervous system predicts the stress level of drivers during driving operation and permits a safe driving by the possibility of an early warning. This stress, taking place during driving, is caused by diverse factors such as changing mood, bio rhythm, fatigue, boredom or disease which can prevent the driver from reaching inappropriate state for driving. In our study, the ECG signal of the driver is extracted and preprocessed in order to perform the HRV analysis. This analysis is accomplished using one of the domain analysis approach such as time, frequency, time-frequency or non-linear methods including Wavelet and STFT. After HRV analysis, several parameters are extracted to build a vector of features for the classification phase. Our experimentation is performed with data issued from 16 different subjects from the Stress Recognition in Automobile Driver database (DRIVEDB). Several classification techniques were investigated including support vector machine with radial basis function (SVM-RBF) kernel, K nearest neighbor (KNN), and radial basis function (RBF) classifiers. Our results indicate that stress detection could be predicted with an accuracy of 83% using SVM-RBF classifier. This also points out the robustness of ECG biometric as an accurate physiological indicator of a driver state. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title Driver stress level detection using HRV analysis en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SAS en_US
dc.author.idnumber 200904853 en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.keywords Heart rate variability en_US
dc.keywords Stress en_US
dc.keywords Vehicles en_US
dc.keywords Electrocardiography en_US
dc.keywords Feature extraction en_US
dc.keywords Resonant frequency en_US
dc.keywords Biomedical monitoring en_US
dc.identifier.doi http://dx.doi.org/10.1006/bbrc.1994.188310.1109/ICABME.2015.7323251 en_US
dc.identifier.ctation Munla, N., Khalil, M., Shahin, A., & Mourad, A. (2015, September). Driver stress level detection using HRV analysis. In Advances in Biomedical Engineering (ICABME), 2015 International Conference on (pp. 61-64). IEEE. en_US
dc.author.email azzam.mourad@lau.edu.lb en_US
dc.conference.date 16-18 Sept. 2015 en_US
dc.conference.pages 61-64 en_US
dc.conference.place Beirut, Lebanon en_US
dc.conference.title 2015 International Conference on Advances in Biomedical Engineering (ICABME) en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://ieeexplore.ieee.org/abstract/document/7323251/ en_US
dc.orcid.id https://orcid.org/0000-0001-9434-5322
dc.publication.date 2015 en_US
dc.author.affiliation Lebanese American University en_US

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