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Alcohol consumption detection through behavioural analysis using intelligent systems
Affiliation:1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;2. Department of Electrical and Computer Engineering, Concordia University, Montreal H3G 1T7, QC, Canada;3. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal H3G 1T7, QC, Canada;1. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC;2. Innovative Information Industry Research Center, School of Computer Science and Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;1. Institute for Infocomm Research, Singapore;2. University of Hong Kong, Hong Kong;3. Centre for Computer and Information Security Research, School of Computer Science and Software Engineering, University of Wollongong, Australia;1. School of Mathematical Science, Anhui University, Hefei, Anhui 230601, China;2. School of Business, Anhui University, Hefei, Anhui 230601, China;1. Research Institute of Computer Science, Technical University of Loja, San Cayetano alto, Loja, Ecuador;2. Department of Computing, Polytechnic University of Madrid, Boadilla del Monte, Madrid, Spain
Abstract:We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.
Keywords:Instrumentation  Automated behavioural analysis  Blood alcohol content  Intelligent systems  Human computer interface  Artificial neural networks
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