(Created page with " == 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...")
 
 
(2 intermediate revisions by the same user not shown)
Line 2: Line 2:
 
== Abstract ==
 
== 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.
+
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.
−
 
+
−
Document type: Part of book or chapter of book
+
−
 
+
−
== Full document ==
+
−
<pdf>Media:Draft_Content_738652529-beopen524-7041-document.pdf</pdf>
+
  
  
Line 15: Line 10:
  
 
* [https://e-archivo.uc3m.es/bitstream/10016/25681/2/estimation_ISAmI_2016_ps.pdf https://e-archivo.uc3m.es/bitstream/10016/25681/2/estimation_ISAmI_2016_ps.pdf]
 
* [https://e-archivo.uc3m.es/bitstream/10016/25681/2/estimation_ISAmI_2016_ps.pdf https://e-archivo.uc3m.es/bitstream/10016/25681/2/estimation_ISAmI_2016_ps.pdf]
 +
 +
* [http://hdl.handle.net/10016/25681 http://hdl.handle.net/10016/25681],
 +
: [https://doi.org/10.1007/978-3-319-40114-0_4 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://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 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://link.springer.com/chapter/10.1007/978-3-319-40114-0_4],
 +
: [https://www.scipedia.com/public/Munoz-Organero_2016a https://www.scipedia.com/public/Munoz-Organero_2016a],
 +
: [https://dx.doi.org/10.1007/978-3-319-40114-0_4 https://dx.doi.org/10.1007/978-3-319-40114-0_4],
 +
: [https://dblp.uni-trier.de/db/conf/isami/isami2016.html#MaganaOAR16 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://rd.springer.com/chapter/10.1007/978-3-319-40114-0_4],
 +
: [https://academic.microsoft.com/#/detail/2464700096 https://academic.microsoft.com/#/detail/2464700096]

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://doi.org/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://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
Back to Top
GET EPUB

Document information

Published on 01/01/2016

Volume 2016, 2016
DOI: 10.1007/978-3-319-40114-0_4
Licence: CC BY-NC-SA license

Document Score

0

Views 2
Recommendations 0

Share this document

Keywords

• • • • • • • • • • • • • •

claim authorship

Are you one of the authors of this document?

I am an author