Drivers State Monitoring: A Case Study on Big Data Analytics
2016 (English)In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, 145-147 p.Conference paper, Poster (Refereed)
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 drivers 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.
Place, publisher, year, edition, pages
2016. Vol. 187, 145-147 p.
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:mdh:diva-33806DOI: 10.1007/978-3-319-51234-1_24ScopusID: 2-s2.0-85011263102OAI: oai:DiVA.org:mdh-33806DiVA: diva2:1048567
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västerås, Sweden
ProjectsVDM - Vehicle Driver Monitoring