A Study on User Recognition Using 2D ECG Image Based on Ensemble Networks for Intelligent Vehicles

Wireless Communications and Mobile Computing. 2019;2019 DOI 10.1155/2019/6458719

 

Journal Homepage

Journal Title: Wireless Communications and Mobile Computing

ISSN: 1530-8669 (Print); 1530-8677 (Online)

Publisher: Hindawi-Wiley

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Min-Gu Kim (Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 61452, Republic of Korea)
Hoon Ko (IT Research Institute, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 61452, Republic of Korea)
Sung Bum Pan (Department of Electronics Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 61452, Republic of Korea)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 28 weeks

 

Abstract | Full Text

IoT enabled smart car era is expected to begin in the near future as convergence between car and IT accelerates. Current smart cars can provide various information and services needed by the occupants via wearable devices or Vehicle to Everything (V2X) communication environment. In order to provide such services, a system to analyze wearable device information on the smart car platform needs to be designed. In this paper a real time user recognition method using 2D ECG (Electrocardiogram) images, a biometric signal that can be obtained from wearable devices, will be studied. ECG (Electrocardiogram) signal can be classified by fiducial point method using feature points detection or nonfiducial point method due to time change. In the proposed algorithm, a CNN based ensemble network was designed to improve performance by overcoming problems like overfitting which occur in a single network. Test results show that 2D ECG image based user recognition accuracy improved by 1%~1.7% for the fiducial point method and by 0.9%~2% for the nonfiducial point method. By showing 13% higher performance compared to the single network in which recognition rate reduction occurs because similar characteristics are shown between classes, capability for use in a smart vehicle platform based user recognition system that requires reliability was demonstrated by the proposed method.