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Detection of Dangerous Driver Health Problems Using HOG-Autoencoder

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 182))

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Abstract

In this paper, we present a method that can be used to detect unexpected driver health problems (e.g. stroke, heart attack, epileptic or similar types of seizures). Obviously, in such cases, the goal is to obtain the recognition results in the shortest possible time. Therefore, the main contribution of the presented method is the speed combined with satisfactory detection results. To achieve these goals, we use the HOG method for fast image feature extraction in the first step. In the second step, an autoencoder network is used to compress the features. Based on the autoencoder reconstruction error, it is then decided whether the driver’s health condition is normal or abnormal. The results seem to be promising for the possible practical deployment.

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Acknowledgments

This work is partially supported by Grants of SGS No. SP2023/072, VSB - Technical University of Ostrava, Czech Republic.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 2172/15, 70800, Ostrava, Czech Republic

    Radovan Fusek, Jakub Halman, Eduard Sojka & Jan Gaura

Authors
  1. Radovan Fusek
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  2. Jakub Halman
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  3. Eduard Sojka
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  4. Jan Gaura
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Corresponding author

Correspondence to Radovan Fusek .

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Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Fusek, R., Halman, J., Sojka, E., Gaura, J. (2023). Detection of Dangerous Driver Health Problems Using HOG-Autoencoder. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_43

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