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A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals

Authors:

Hamidur Rahman, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Bertil Hök

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

The 3rd EAI International Conference on IoT Technologies for HealthCare

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.

Bibtex

@inproceedings{Rahman4495,
author = {Hamidur Rahman and Shaibal Barua and Mobyen Uddin Ahmed and Shahina Begum and Bertil H{\"o}k},
title = {A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals},
month = {October},
year = {2016},
booktitle = {The 3rd EAI International Conference on IoT Technologies for HealthCare},
url = {http://www.es.mdu.se/publications/4495-}
}