ML Anthology

CardioWheel: ECG Biometrics on the Steering Wheel

André Lourenço, Ana Priscila Alves, Carlos Carreiras, Rui Policarpo Duarte, Ana L. N. Fred
ECML-PKDD 2015 pp. 267-270
doi:10.1007/978-3-319-23461-8_27 /ecmlpkdd/2015/lourenco2015ecmlpkdd-cardiowheel/

Abstract

Monitoring physiological signals while driving is a recent trend in the automotive industry. We present CardioWheel, a state-of-the-art machine learning solution for driver biometrics based on electrocardiographic signals (ECG). The presented system pervasively acquires heart signals from the users hands through sensors embedded in the steering wheel, to recognize the driver’s identity. It combines unsupervised and supervised machine learning algorithms, and is being tested in real-world scenarios, illustrating one of the potential uses of this technology.

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Cite

Text

Lourenço et al. "CardioWheel: ECG Biometrics on the Steering Wheel." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23461-8_27

Markdown

[Lourenço et al. "CardioWheel: ECG Biometrics on the Steering Wheel." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/lourenco2015ecmlpkdd-cardiowheel/) doi:10.1007/978-3-319-23461-8_27

BibTeX

@inproceedings{lourenco2015ecmlpkdd-cardiowheel,
  title     = {{CardioWheel: ECG Biometrics on the Steering Wheel}},
  author    = {Lourenço, André and Alves, Ana Priscila and Carreiras, Carlos and Duarte, Rui Policarpo and Fred, Ana L. N.},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2015},
  pages     = {267-270},
  doi       = {10.1007/978-3-319-23461-8_27},
  url       = {https://mlanthology.org/ecmlpkdd/2015/lourenco2015ecmlpkdd-cardiowheel/}
}