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

Authors:

Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

The 3rd EAI International Conference on IoT Technologies for HealthCare

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.

Bibtex

@inproceedings{Barua4501,
author = {Shaibal Barua and Shahina Begum and Mobyen Uddin Ahmed},
title = {Driver’s State Monitoring: A Case Study on Big Data Analytics},
month = {October},
year = {2016},
booktitle = {The 3rd EAI International Conference on IoT Technologies for HealthCare},
url = {http://www.es.mdu.se/publications/4501-}
}