Articles

SAfeDJ: A Crowd-Cloud Codesign Approach to Situation-Aware Music Delivery for Drivers

X. HU, J. DENG, J. ZHAO, R. WANG, X. LI, V. LEUNG, Y. KWOK

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

October 2015, vol. 12, n°1

Departments: Information Systems and Operations Management, GREGHEC (CNRS)

Keywords: Smartphones, Context, Crowdsensing, Driving, Cloud, Music mood


Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.


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