Latest revision as of 17:42, 28 January 2021

Abstract

Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st–3rd Stress is one of the most important factors in car accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to predict stress level on a road. Prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section, the road state (weather and traffic), and the previous driving made by the driver. We employ this algorithm to build a speed assistant. The solution provides an optimum average speed for each road stage that minimizes the stress. Validation experiment has been conducted using five different datasets with 100 samples. The proposal is able to predict the stress level given the average speed by 84.20% on average. The system reduces the heart rate (15.22%) and the aggressiveness of driving. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap. The research leading to these results has received funding from the “HERMES-SMART DRIVER/CITIZEN” projects TIN2013-46801-C4-2-R /1-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte.

Original document

The different versions of the original document can be found in:

• https://e-archivo.uc3m.es/bitstream/10016/25681/2/estimation_ISAmI_2016_ps.pdf

• http://hdl.handle.net/10016/25681,

https://doi.org/10.1007/978-3-319-40114-0_4

• http://link.springer.com/content/pdf/10.1007/978-3-319-40114-0_4,

http://dx.doi.org/10.1007/978-3-319-40114-0_4 under the license http://www.springer.com/tdm

• https://link.springer.com/chapter/10.1007/978-3-319-40114-0_4,

https://www.scipedia.com/public/Munoz-Organero_2016a,

https://dx.doi.org/10.1007/978-3-319-40114-0_4,

https://dblp.uni-trier.de/db/conf/isami/isami2016.html#MaganaOAR16,

https://rd.springer.com/chapter/10.1007/978-3-319-40114-0_4,

https://academic.microsoft.com/#/detail/2464700096