TY - GEN
T1 - Mood-fatigue analyzer
T2 - 1st ACM Workshop on Middleware for Context-Aware Applications in the IoT, M4IOT 2014 - In conjunction with ACM/IFIP/USENIX ACM International Middleware Conference
AU - Hu, Wenyan
AU - Hu, Xiping
AU - Deng, Jun Qi
AU - Zhu, Chunsheng
AU - Fotopoulos, Georgios
AU - Ngai, Edith C.H.
AU - Leung, Victor C.M.
N1 - Publisher Copyright:
Copyright 2014 ACM.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - Nowadays more and more organizations focus on reducing traffic accidents and defensive measures for safe driving. The vigilance level (e.g., negative emotion and fatigue) also accounts for the road injuries. Till now, there is no systematic solution for different mobile devices that can effectively infer the mood and fatigue of drivers in real-time or conveniently be used by drivers, nor incentive scheme for drivers in large scale to stimulate their positive and secure driving collaboratively with friends in a social context. In this paper, we propose the Mood-Fatigue Analyzer (MFA), a systematic solution that can be used in different middlewares on mobile devices, which can transform the data from sensors to context-aware mobile sensing applications for safe driving. The MFA employs multidimensional methods to get the drivers' real-time mood and fatigue information by sensors using the Internet of Things (IoT) deployed in and out of cars. Besides promoting safe driving with integrated sensors, the MFA could be built on a multi-tier vehicular social network (VSN) platform, which enables communication among drivers in a social context via cloud platform. Architecture implementation and experimental results of the MFA have demonstrated its desired functionalities and efficiency in drivers' daily lives and real-world deployment.
AB - Nowadays more and more organizations focus on reducing traffic accidents and defensive measures for safe driving. The vigilance level (e.g., negative emotion and fatigue) also accounts for the road injuries. Till now, there is no systematic solution for different mobile devices that can effectively infer the mood and fatigue of drivers in real-time or conveniently be used by drivers, nor incentive scheme for drivers in large scale to stimulate their positive and secure driving collaboratively with friends in a social context. In this paper, we propose the Mood-Fatigue Analyzer (MFA), a systematic solution that can be used in different middlewares on mobile devices, which can transform the data from sensors to context-aware mobile sensing applications for safe driving. The MFA employs multidimensional methods to get the drivers' real-time mood and fatigue information by sensors using the Internet of Things (IoT) deployed in and out of cars. Besides promoting safe driving with integrated sensors, the MFA could be built on a multi-tier vehicular social network (VSN) platform, which enables communication among drivers in a social context via cloud platform. Architecture implementation and experimental results of the MFA have demonstrated its desired functionalities and efficiency in drivers' daily lives and real-world deployment.
KW - Cloud
KW - Context-aware
KW - Vehicular sensor application
UR - http://www.scopus.com/inward/record.url?scp=84943242983&partnerID=8YFLogxK
U2 - 10.1145/2676743.2676747
DO - 10.1145/2676743.2676747
M3 - Conference contribution
AN - SCOPUS:84943242983
T3 - Proceedings of the 1st ACM Workshop on Middleware for Context-Aware Applications in the IoT, M4IOT 2014 - In conjunction with ACM/IFIP/USENIX ACM International Middleware Conference
SP - 19
EP - 24
BT - Proceedings of the 1st ACM Workshop on Middleware for Context-Aware Applications in the IoT, M4IOT 2014 - In conjunction with ACM/IFIP/USENIX ACM International Middleware Conference
PB - Association for Computing Machinery
Y2 - 9 December 2014
ER -
