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

被引：34

作者：

Hu, Xiping ^{[1
]}

Deng, Junqi ^{[2
]}

Zhao, Jidi ^{[3
]}

Hu, Wenyan ^{[4
]}

Ngai, Edith C. -H. ^{[5
]}

Wang, Renfei ^{[6
]}

Shen, Johnny ^{[1
]}

Liang, Min

Li, Xitong ^{[7
]}

Leung, Victor C. M. ^{[1
]}

Kwok, Yu-Kwong ^{[2
]}

机构：

[1] Univ British Columbia, Vancouver, BC V5Z 1M9, Canada

[2] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China

[3] E China Normal Univ, Shanghai, Peoples R China

[4] Nankai Univ, Tianjin, Peoples R China

[5] Uppsala Univ, Uppsala, Sweden

[6] IBM Canada, Markham, ON, Canada

[7] HEC Paris, Paris, France

来源：

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS | 2015年 / 12卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Design; Algorithms; Experimentation; Smartphones; crowdsensing; cloud; music mood; context; driving; COMPUTING MOTIVATION; MOBILE; SYSTEM; STATE;

D O I：

10.1145/2808201

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：24

共 37 条

[1] Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].