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Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring

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Internet of Things (IoT) Technologies for HealthCare (HealthyIoT 2017)

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Abstract

This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analysed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

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Notes

  1. 1.

    https://www.vti.se/en/research-areas/vtis-driving-simulators/.

  2. 2.

    www.dewesoft.com.

  3. 3.

    https://www.knime.com/.

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Acknowledgments

The authors would like to acknowledge VINNOVA (Swedish Governmental Agency for Innovation Systems) for supporting the “Vehicle Driver Monitoring” project. The authors would also like to acknowledge our project partners Volvo Car Corporation and the Swedish National Road and Transport Research Institute (VTI).

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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, 72123, Västerås, Sweden

    Shaibal Barua, Mobyen Uddin Ahmed & Shahina Begum

Authors
  1. Shaibal Barua
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  2. Mobyen Uddin Ahmed
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  3. Shahina Begum
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Corresponding author

Correspondence to Shaibal Barua .

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Editors and Affiliations

  1. Mälardalen University, Västerås, Sweden

    Mobyen Uddin Ahmed

  2. Mälardalen University, Västerås, Sweden

    Shahina Begum

  3. University of Angers, Angers, France

    Jean-Baptiste Fasquel

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Barua, S., Ahmed, M.U., Begum, S. (2018). Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring. In: Ahmed, M., Begum, S., Fasquel, JB. (eds) Internet of Things (IoT) Technologies for HealthCare. HealthyIoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-319-76213-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-76213-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76212-8

  • Online ISBN: 978-3-319-76213-5

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