Research Article
A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals
@INPROCEEDINGS{10.1007/978-3-319-51234-1_4, author={Hamidur Rahman and Shaibal Barua and Mobyen Ahmed and Shahina Begum and Bertil H\o{}k}, title={A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals}, proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers}, proceedings_a={HEALTHYIOT}, year={2017}, month={1}, keywords={Physiological signals Alcoholic detection Case-based reasoning}, doi={10.1007/978-3-319-51234-1_4} }- Hamidur Rahman
Shaibal Barua
Mobyen Ahmed
Shahina Begum
Bertil Hök
Year: 2017
A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-51234-1_4
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 or 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.
