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<identifier>oai:HAL:hal-00849971v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Real Time Drunkness Analysis Through Games Using Artificial Neural Networks</title>
<creator>Robinel, Audrey</creator>
<creator>Puzenat, Didier</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Proceedings of the 2011 International Conference on Advances in Computer-Human Interactions</source>
<source>International Conference on Advances in Computer-Human Interactions</source>
<coverage>Gosier, Guadeloupe</coverage>
<identifier>hal-00849971</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00849971</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00849971/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00849971/file/article.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00849971</source>
<source>International Conference on Advances in Computer-Human Interactions, Feb 2011, Gosier, Guadeloupe. pp.206-211, 2011</source>
<language>en</language>
<subject lang=en>User interfaces</subject>
<subject lang=en>Games</subject>
<subject lang=en>Neural network applications</subject>
<subject lang=en>Cognitive sciences</subject>
<subject lang=en>Psychology</subject>
<subject lang=en>Human factors</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this paper, we describe a blood alcohol content estimation prototype based on a comportment analysis performed by artificial neural networks. We asked to subjects that had drunk alcohol to play a video-game after having measured their blood alcohol content with a breathalyser. A racing game was modified so that it could provide various data related to the use of the controls by the player. Using the collected data, we trained our neural network in order to be able to determine whether or not the subject had exceeded a blood alcohol content threshold. We also succeeded in estimating this blood alcohol content with a mean error of 0.1g/l. We could perform those estimations independently of the track played among the two ones used. It was also performed in "real time", e.g., using only the data collected within the last 10 seconds of playing.</description>
<date>2011-02-23</date>
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