Mood-fatigue analyzer: towards context-aware mobile sensing applications for safe driving

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.