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Open AccessArticle

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

1
Faculdade de Engenharia, Universidade do Porto; R. Dr. Roberto Frias, 4200-465 Porto, Portugal
2
INESC-TEC; R. Dr. Roberto Frias, 4200-465 Porto, Portugal
3
CardioID Technologies Lda.; R. Adriano Correia de Oliveira 4A F1, 1600-312 Lisboa, Portugal
4
Instituto Superior de Engenharia de Lisboa; R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(10), 2228; https://doi.org/10.3390/s17102228
Received: 23 August 2017 / Revised: 16 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
(This article belongs to the Section Biosensors)
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings. View Full-Text
Keywords: authentication; biometrics; continuous; electrocardiogram (ECG); identification; off-the-person; outlier detection; signal denoising authentication; biometrics; continuous; electrocardiogram (ECG); identification; off-the-person; outlier detection; signal denoising
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Pinto, J.R.; Cardoso, J.S.; Lourenço, A.; Carreiras, C. Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel. Sensors 2017, 17, 2228.

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