Driver’s State Monitoring: A Case Study on Big Data Analytics

Shaibal Barua; Shahina Begum; Mobyen Ahmed

Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers

Research Article

Driver’s State Monitoring: A Case Study on Big Data Analytics

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@INPROCEEDINGS{10.1007/978-3-319-51234-1_24,
    author={Shaibal Barua and Shahina Begum and Mobyen Ahmed},
    title={Driver’s State Monitoring: A Case Study on Big Data Analytics},
    proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers},
    proceedings_a={HEALTHYIOT},
    year={2017},
    month={1},
    keywords={},
    doi={10.1007/978-3-319-51234-1_24}
}

Shaibal Barua
Shahina Begum
Mobyen Ahmed
Year: 2017
Driver’s State Monitoring: A Case Study on Big Data Analytics
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-51234-1_24

Shaibal Barua¹^,*, Shahina Begum¹^,*, Mobyen Ahmed¹^,*

1: Mälardalen University

*Contact email: shaibal.barua@mdh.se, shahina.begum@mdh.se, mobyen.ahmed@mdh.se

Abstract

Driver’s distraction, inattention, sleepiness, stress, etc. are identified as causal factors of vehicle crashes and accidents. Today, we know that physiological signals are convenient and reliable measures of driver’s impairments. Heterogeneous sensors are generating vast amount of signals, which need to be handled and analyzed in a big data scenario. Here, we propose a big data analytics approach for driver state monitoring using heterogeneous data that are coming from multiple sources, i.e., physiological signals along with vehicular data and contextual information. These data are processed and analyzed to aware impaired vehicle drivers.

Published: 2017-01-20
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-51234-1_24