Munoz-Organero 2016a - Revision history

Scipediacontent at 16:42, 28 January 2021

2021-01-28T16:42:30Z

Scipediacontent at 12:08, 21 January 2021

2021-01-21T12:08:30Z

Scipediacontent: Scipediacontent moved page Draft Content 738652529 to Munoz-Organero 2016a

2020-10-14T12:22:05Z

Scipediacontent moved page Draft Content 738652529 to Munoz-Organero 2016a

Scipediacontent: Created page with " == Abstract == Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st3rd Stress is one of the most important factors..."

2020-10-14T12:22:02Z

Created page with " == Abstract == Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st3rd Stress is one of the most important factors..."

New page

== Abstract ==

Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st3rd 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>

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

← Older revision		Revision as of 16:42, 28 January 2021
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	== Abstract ==		== Abstract ==

−	Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June ~~1stâ3rd~~ 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 ~~âHERMES~~-SMART DRIVER/~~CITIZENâ~~ projects TIN2013-46801-C4-2-R /1-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de ~~EducaciÃ³n~~ 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.

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 == Abstract ==
-Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016),  Seville, Spain on June 1st3rd 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 1stâ3rd 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   âHERMES-SMART DRIVER/CITIZENâ projects TIN2013-46801-C4-2-R /1-R funded by  the  Spanish  MINECO,  from  the  grant  PRX15/00036  from  the  Ministerio  de  EducaciÃ³n   Cultura y Deporte.
-−
-Document type: Part of book or chapter of book
-−
-== Full document ==
-<pdf>Media:Draft_Content_738652529-beopen524-7041-document.pdf</pdf>
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 * [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]

← Older revision	Revision as of 12:22, 14 October 2020
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Munoz-Organero 2016a - Revision history

Scipediacontent at 16:42, 28 January 2021

Scipediacontent at 12:08, 21 January 2021

Scipediacontent: Scipediacontent moved page Draft Content 738652529 to Munoz-Organero 2016a

Scipediacontent: 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..."

Scipediacontent: Created page with " == Abstract == Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st3rd Stress is one of the most important factors..."