Drivers State Monitoring: A Case Study on Big Data Analytics
In: VDM - Vehicle Driver Monitoring The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16 Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2016, S. 145-147
Online
unknown
Zugriff:
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.
Titel: |
Drivers State Monitoring: A Case Study on Big Data Analytics
|
---|---|
Autor/in / Beteiligte Person: | Barua, Shaibal ; Begum, Shahina ; Ahmed, Mobyen Uddin |
Link: | |
Zeitschrift: | VDM - Vehicle Driver Monitoring The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16 Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2016, S. 145-147 |
Veröffentlichung: | 2016 |
Medientyp: | unknown |
ISSN: | 1867-8211 (print) |
DOI: | 10.1007/978-3-319-51234-1_24 |
Sonstiges: |
|