TY - GEN
T1 - A survey on mobile sensing based mood-fatigue detection for drivers
AU - Tu, Wei
AU - Wei, Lei
AU - Hu, Wenyan
AU - Sheng, Zhengguo
AU - Nicanfar, Hasen
AU - Hu, Xiping
AU - Ngai, Edith C.H.
AU - Leung, Victor C.M.
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.
PY - 2016
Y1 - 2016
N2 - The rapid development of the Internet of Things (IoT) has provided innovative solutions to reduce traffic accidents caused by fatigue driving. When drivers are in bad mood or tired, their vigilance level decreases, which may prolong the reaction time to emergency situation and lead to serious accidents. With the help of mobile sensing and mood-fatigue detection, drivers’ moodfatigue status can be detected while driving, and then appropriate measures can be taken to eliminate the fatigue or negative mood to increase the level of vigilance. This paper presents the basic concepts and current solutions of moodfatigue detection and some common solutions like mobile sensing and cloud computing techniques. After that, we introduce some emerging platforms which designed to promote safe driving. Finally, we summarize the major challenges in mood-fatigue detection of drivers, and outline the future research directions.
AB - The rapid development of the Internet of Things (IoT) has provided innovative solutions to reduce traffic accidents caused by fatigue driving. When drivers are in bad mood or tired, their vigilance level decreases, which may prolong the reaction time to emergency situation and lead to serious accidents. With the help of mobile sensing and mood-fatigue detection, drivers’ moodfatigue status can be detected while driving, and then appropriate measures can be taken to eliminate the fatigue or negative mood to increase the level of vigilance. This paper presents the basic concepts and current solutions of moodfatigue detection and some common solutions like mobile sensing and cloud computing techniques. After that, we introduce some emerging platforms which designed to promote safe driving. Finally, we summarize the major challenges in mood-fatigue detection of drivers, and outline the future research directions.
KW - Mobile sensing
KW - Mood-fatigue detection
KW - Vehicular sensor application
UR - http://www.scopus.com/inward/record.url?scp=84978215420&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-33681-7_1
DO - 10.1007/978-3-319-33681-7_1
M3 - Conference contribution
AN - SCOPUS:84978215420
SN - 9783319336800
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 3
EP - 15
BT - Smart City 360 - 1st EAI International Summit, Smart City 360, Revised Selected Papers
A2 - Krutilova, Veronika
A2 - Cagáňová, Dagmar
A2 - Špirková, Daniela
A2 - Golej, Julius
A2 - Nguyen, Kim
A2 - Lenort, Radim
A2 - Holman, David
A2 - Staš, David
A2 - Wicher, Pavel
A2 - Leon-Garcia, Alberto
PB - Springer Verlag
T2 - International Conference on Sustainable Solutions Beyond Mobility of Goods, SustainableMoG 2015
Y2 - 13 October 2015 through 14 October 2015
ER -
