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

João Ribeiro Pinto1, Jaime S Cardoso2,3, André Lourenço4,5

  • 1Faculdade de Engenharia, Universidade do Porto; R. Dr. Roberto Frias, 4200-465 Porto, Portugal. ribeiro.pinto@fe.up.pt.

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Summary

Electrocardiogram (ECG) signals from steering wheels enable continuous driver recognition. Signal processing and machine learning classifiers achieve high accuracy, proving suitability for noisy driving environments.

Keywords:
authenticationbiometricscontinuouselectrocardiogram (ECG)identificationoff-the-personoutlier detectionsignal denoising

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Area of Science:

  • Biometrics
  • Signal Processing
  • Machine Learning

Background:

  • Electrocardiogram (ECG) signals acquired via steering wheels offer potential for seamless driver recognition.
  • The inherent noise in driving environments significantly degrades ECG signal quality.

Purpose of the Study:

  • To enhance the quality of ECG signals captured through steering wheels.
  • To develop and evaluate a robust system for driver identification and authentication using processed ECG signals.

Main Methods:

  • ECG signal enhancement using Savitzky-Golay and moving average filters.
  • Outlier removal via normalized cross-correlation and clustering.
  • Feature extraction using Discrete Cosine Transform (DCT) and Haar transform.
  • Classification using Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM).

Main Results:

  • Achieved a high identification rate (IDR) of 94.9% and an authentication equal error rate (EER) of 2.66%.
  • Demonstrated lower performance with limited training data (70.9% IDR, 11.8% EER).
  • The proposed method's performance is comparable to state-of-the-art techniques.

Conclusions:

  • The developed method is suitable for biometric recognition using driving ECG signals.
  • Future developments could enable continuous, seamless recognition in highly noisy settings.
  • ECG-based driver recognition shows promise for enhanced automotive security and personalization.