Estimating the stress for drivers and passengers using deep learning

Corcoba Magaña, Víctor; Muñoz Organero, Mario; Arias Fisteus, Jesús; Sánchez Fernández, Luis

Publication:
Estimating the stress for drivers and passengers using deep learning

dc.affiliation.dpto	UC3M. Departamento de Ingeniería Telemática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)	es
dc.contributor.author	Corcoba Magaña, Víctor
dc.contributor.author	Muñoz Organero, Mario
dc.contributor.author	Arias Fisteus, Jesús
dc.contributor.author	Sánchez Fernández, Luis
dc.date.accessioned	2017-10-26T13:54:51Z
dc.date.available	2017-10-26T13:54:51Z
dc.date.issued	2016
dc.description	Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016	es
dc.description.abstract	The number of vehicles in circulation has become a problem both for safety and for the citizens health. Public transport is a solution to reduce its impact on the environment. One of the keys to encourag e users to use it is to improve comfort. On the other hand, numerous studies highlight that drivers are more likely to suffer physical and psychological illnesses due to the sedentary nature of this work and workload . In this paper, we propose a model to p redict the stress level on drivers and passengers. The solution is based on deep learning algorithms. The proposal employs the Heart Rate Variability (HRV) and telemetry from the vehicle in order to anticipate the incoming stress . It has been validated in a real environment on distinct routes. The results show that it predict s the stress by 86 % on drivers and 92% on passengers. This algorithm could be used to develop driving assistants that recommend actions to smooth driving, reducing the work load and the passenger stress.	es
dc.description.sponsorship	The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte.	es
dc.format.extent	6	es
dc.format.mimetype	application/pdf
dc.identifier.bibliographicCitation	Falomir, Zoe; Ortega, Juan Antonio. Proceedings of the XVIII Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence, Almería, Spain, 23-29 June, 2016. Ceur Workshop Proceedings (pp. 1-6)	es
dc.identifier.issn	1613-0073 (online)
dc.identifier.publicationfirstpage	1	es
dc.identifier.publicationlastpage	6	es
dc.identifier.publicationtitle	Proceedings of the XVIII Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence (JARCA 2016), Almería, Spain, June 23-29, 2016	es
dc.identifier.uri	https://hdl.handle.net/10016/25691
dc.identifier.uxxi	CC/0000026655
dc.language.iso	eng	es
dc.publisher	CEUR-WS.org	es
dc.relation.eventdate	2016, 23rd-29th June	es
dc.relation.eventplace	Almería, Spain	es
dc.relation.eventtitle	XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence	en
dc.relation.ispartofseries	Ceur Workshop Proceedings, 1812	es
dc.relation.projectID	Gobierno de España. TIN2013-46801-C4-2-R	es
dc.relation.projectID	Gobierno de España. PRX15/00036	es
dc.relation.publisherversion	http://ceur-ws.org/Vol-1812/JARCA16-paper-1.pdf	es
dc.rights	© 2016 for the individual papers by their authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	es
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Telecomunicaciones	es
dc.subject.other	Stress level prediction	es
dc.subject.other	Stress-friendly driving behavior	es
dc.subject.other	Stress level classification	es
dc.subject.other	Heart Rate Variability	es
dc.subject.other	Machine learning	es
dc.subject.other	Deep learning	es
dc.subject.other	Algorithms	es
dc.title	Estimating the stress for drivers and passengers using deep learning	es
dc.type	conference proceedings	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication