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Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring

Authors:

Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

4th EAI International Conference on IoT Technologies for HealthCare

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 analyzed 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.

Bibtex

@inproceedings{Barua4867,
author = {Shaibal Barua and Mobyen Uddin Ahmed and Shahina Begum},
title = {Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring},
month = {October},
year = {2017},
booktitle = {4th EAI International Conference on IoT Technologies for HealthCare},
url = {http://www.es.mdu.se/publications/4867-}
}