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Personal Car Driver Black Box: A Wearable System for Data Log and Prediction Based on EVOS Paradigms

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

Car driver’s performance is affected by physiologic behavior, causing automotive accidents. A system that predicts incoming behaviors is proposed considering vital signs as data to infer by Evolving Systems (EVOS) paradigms and several behaviors as prediction targets. A black-box like approach is proposed. It implements data loging of the vital signs and it applies an evolving prediction paradigm. Long term data log enables post-event analysis and data set building for supervised learning for the prediction paradigms.

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References

  1. NHTSA: Drowsy driving. Published by NHTSA’s National Center for Statistics and Analysis. 1200 New Jersey Avenue SE, Washington, DC 20590 (2011)

    Google Scholar 

  2. Malcangi, M., Smirne, S.: Fuzzy-logic inference for early detection of sleep onset. Neural Comput. Appl. 27, 41–50 (2015)

    Google Scholar 

  3. Dorfman, G.F., Baharav, A., Cahan, C., Akselrod, S.: Early detection of falling asleep at the wheel: a heart rate variability approach. Comput. Cardiol. 35, 1109–1112 (2008)

    Google Scholar 

  4. Stutts, J.C.: Sleep Deprivation Countermeasures. National Academy Press, Wascinton D.C. (2000)

    Google Scholar 

  5. Straczkiewicz, M., Urbanek, J.K., Crainiceanu, C.M., Harezlak, J.: Automatic car driving detection using raw accelerometry data. Physiol. Meas. 37(10), 1757–1769 (2016)

    Article  Google Scholar 

  6. Quintana, S.D., Heathers, J.A.J.: Considerations in the assessment of heart rate variability in behavioral research. Front. Psychol. 5, 805 (2014)

    Article  Google Scholar 

  7. Malcangi, M.: Applying evolutionary methods for early prediction of sleep onset. Neural Comput. Appl. 27, 1165–1173 (2016). https://doi.org/10.1007/s00521-015-1928-6

    Article  Google Scholar 

  8. Kasabov, N.: Evolving fuzzy neural network – algorithms, applications and biological motivation. In: Yamakawa, T., Matsumoto, G. (eds.) Methodologies for Conception, Design and Application of the Soft Computing. World Computing, pp. 271–274 (1998)

    Google Scholar 

  9. Kasabov, N.: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence. Springer Series on Neurosystems. Springer, Berlin (2019)

    Book  Google Scholar 

  10. Broeders, J.H.: Wearable electronic devices monitor vital signs activity level and more. Analog Dialog (December 2014)

    Google Scholar 

  11. Palhmi, J., Broeders, J.H.: Optical integration without compromises (1995). https://www.analog.com/en/technical-articles/optical-integration-without-compromises.html

  12. Broeders, J.H.: The sensors behind the GEN II wearable device, Technical Article, https://www.analog.com/media/en/technical-documentation/tech-articles/the-sensors-behind-the-GEN-II-wearable-device.pdf

  13. https://Kedri.aut.nz/areas-of-expertise/data-mining-and-support/neucom

  14. Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12, 16937–16953 (2012)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Università degli Studi di Milano, Milan, Italy

    Mario Malcangi

Authors
  1. Mario Malcangi
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Correspondence to Mario Malcangi .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Malcangi, M. (2020). Personal Car Driver Black Box: A Wearable System for Data Log and Prediction Based on EVOS Paradigms. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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