Abstract
This paper presents a case-based classification system for alcohol detection using physiological parameters. Here, four physiological parameters e.g. Heart Rate Variability (HRV), Respiration Rate (RR), Finger Temperature (FT), and Skin Conductance (SC) are used in a Case-based reasoning (CBR) system to detect alcoholic state. In this study, the participants are classified into two groups as drunk or sober. The experimental work shows that using the CBR classification approach the obtained accuracy for individual physiological parameters e.g., HRV is 85%, RR is 81%, FT is 95% and SC is 86%. On the other hand, the achieved accuracy is 88% while combining the four parameters i.e., HRV, RR, FT and SC using the CBR system. So, the evaluation illustrates that the CBR system based on physiological sensor signal can classify alcohol state accurately when a person is under influence of at least 0.2 g/l of alcohol.
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Acknowledgement
The authors would like to acknowledge the Swedish Knowledge Foundation (KKS), Hök instrument AB, Volvo Car Corporation (VCC), The Swedish National Road and Transport Research Institute (VTI), Autoliv AB, Prevas AB Sweden, Hässlögymnasiets, Västerås and all the test subjects for their support of the research projects in this area.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Rahman, H., Barua, S., Ahmed, M.U., Begum, S., Hök, B. (2016). A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_4
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