research-article

Towards In Time Music Mood-Mapping for Drivers: A Novel Approach

Authors:

Arun Sai Krishnan,

Edith C.-H. Ngai,

Victor C.M. Leung,

Yu-kwong KwokAuthors Info & Claims

DIVANet '15: Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

Pages 59 - 66

https://doi.org/10.1145/2815347.2815352

Published: 02 November 2015 Publication History

Abstract

Road safety is a huge concern due to the large number of fatalities and injuries caused by road accidents. Research has shown that fatigue can adversely affect driving performance and increase risk of road accidents. It has been shown that driving performance is enhanced by stress-relieving music which thereby promotes safer driving. Context-aware music delivery systems promote safer driving through intelligent music recommendations based on contextual knowledge. Two key aspects of situation-aware music delivery are effectiveness and efficiency of music recommendation. Efficiency is a critical aspect in real-time context based music recommendation as the music delivery system should quickly sense any change in the situation and deliver suitable music before the sensed context-data becomes obsolete. We focus on the efficiency of situation-aware music delivery systems in this paper. Music mood-mapping is a process which helps in understanding the mood of a song and is hence used in situation-aware music recommendation systems. This process requires a large processing time due to the complex calculations and large sizes of music files involved. Hence, optimizing this process is the key to improving the efficiency of context-aware music delivery systems. Here, we propose a novel cloud and crowd-sensing based approach to considerably optimize the efficiency of situation-aware music delivery systems.

References

[1]

World report on road traffic injury prevention, 2014, World Health Organization (WHO). Available: http://www.who.int/violence_injury_prevention/publications/road_traffic/world_report/en

[2]

A study by Transport - European Commission. Available: http://ec.europa.eu/transport/road_safety/specialist/knowledge/fatique/index_en.htm

[3]

M. Zwaag, C. Dijksterhuis, D. Waard, B. L. J. M. Mulder, J. H. D. M. Westerink, and K. A. Brookhuis. The influence of music on mood and performance while driving. Ergonomics. 55, 1 (2012), 12--22.

[4]

X. Hu, J. Deng, J. Zhao, W. Hu, E. C.-H. Ngai, R. Wang, J. Shen, M. Liang, X. Li, V. C.M. Leung, and Y. Kwok. SAfeDJ: A Crowd-Cloud Co-design Approach to Situation-aware Music Delivery for Drivers. To appear in ACM Transactions on Multimedia Computing, Communications and Applications, 2015.

Digital Library

[5]

X. Hu, J. Deng, W. Hu, G. Fotopoulos, E.C.-H. Ngai, Z. Sheng, M. Liang, X. Li, V.C.M. Leung, and S. Fels.2014. SAfeDJ Community: Situation-Aware In-Car Music Delivery for Safe Driving. In Proc. ACM MobiCom, 2014.

Digital Library

[6]

F. Aalamifar, G. Vijay, P. Khoozani, and M. Ibnkahla. Cognitive wireless sensor networks for highway safety. In Proc. ACM MSWiM-DIVANet symp, 2011, pp. 55--60.

Digital Library

[7]

F. Silva, A. Boukerche, T. Silva, L. Ruiz, E. Cerqueira, and A. Loureiro. Content replication and delivery in vehicular networks. In Proc. ACM MSWiM-DIVANet symp, 2014, pp. 127--132.

Digital Library

[8]

S. Nirjon, R. F. Dickerson, Q. Li, P. Asare, J. A. Stankovic, D. Hong, and F. Zhao. Musicalheart: A hearty way of listening to music. In Proc. ACM SenSys, 2012, pp. 43--56.

Digital Library

[9]

X. Hu, T.H.S. Chu, V.C.M. Leung, E. C.-H. Ngai, P. Kruchten, and H.C.B. Chan. A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions. IEEE Communications Surveys & Tutorials, vol. 17, 2015.

[10]

(2015) X. Hu. Context-aware mobile crowdsensing in mobile social networks. Available: www.mobilesoa.appspot.com

[11]

C. Wang and Z. Li. A computation offloading scheme on handheld devices. J. Parallel Distrib. Comput, vol. 64, no. 6, June 2004, pp. 740--746.

Digital Library

[12]

Liu et al. Energy efficient GPS sensing with cloud offloading. In Proc. ACM SenSys. 2012, pp. 85--98.

Digital Library

[13]

A. P. Miettinen and J. K. Nurminen. Energy Efficiency of Mobile Clients in Cloud Computing. In Proc. USENIX HotCloud, 2010.

Digital Library

[14]

(2015) Sequence Matcher {Online}. Available: https://docs.python.org/2/library/difflib.html

[15]

Z. Wei, F. Yu, A. Boukerche. Trust based security enhancements for vehicular ad hocnetworks. In Prof. ACM MSWiM-DIVANet symp, 2014, pp. 103--109.

Digital Library

[16]

X. Hu, L. Wang, Z. Sheng, P. TalebiFard, Li Zhou, J. Liu, and V.C.M. Leung. Towards a service centric contextualized vehicular cloud. In Proc. ACM MSWiM-DIVANet symp, 2014, pp. 73--80.

Digital Library

[17]

(2015) Echoprint {Online}. Available: http://echoprint.me/

Cited By

Wang DJia SPei XHan CYao DLiu D(2024)DERNet: Driver Emotion Recognition Using Onboard CameraIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2023.333388216:2(117-132)Online publication date: Mar-2024
https://doi.org/10.1109/MITS.2023.3333882
Lal KLekhraj (2024)Automatic multimedia classification based on mood recognition of drivers in Internet-of-vehicle using fog computingWireless Networks10.1007/s11276-024-03872-5Online publication date: 16-Nov-2024
https://doi.org/10.1007/s11276-024-03872-5
Koch KMishra VLiu SBerger TFleisch EKotz DWortmann F(2021)When Do Drivers Interact with In-Vehicle Well-being Interventions?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481165:1(1-30)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448116
Show More Cited By

Index Terms

Towards In Time Music Mood-Mapping for Drivers: A Novel Approach
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Conferences

DIVANet '15: Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

November 2015

124 pages

ISBN:9781450337601

DOI:10.1145/2815347

General Chair:
Robson E. De Grande
NSERC DIVA Research Centre, Canada
,
Program Chairs:
Mirela S.M.A. Notare
Faculdade de Tecnologia em Transporte Aéreo, Brazil
,
Amir Darehshoorzadeh
NSERC DIVA Research Centre, Canada

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2015

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MSWiM'15

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SIGSIM

MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2 - 6, 2015

Cancun, Mexico

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Overall Acceptance Rate 70 of 308 submissions, 23%

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Cited By

Wang DJia SPei XHan CYao DLiu D(2024)DERNet: Driver Emotion Recognition Using Onboard CameraIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2023.333388216:2(117-132)Online publication date: Mar-2024
https://doi.org/10.1109/MITS.2023.3333882
Lal KLekhraj (2024)Automatic multimedia classification based on mood recognition of drivers in Internet-of-vehicle using fog computingWireless Networks10.1007/s11276-024-03872-5Online publication date: 16-Nov-2024
https://doi.org/10.1007/s11276-024-03872-5
Koch KMishra VLiu SBerger TFleisch EKotz DWortmann F(2021)When Do Drivers Interact with In-Vehicle Well-being Interventions?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481165:1(1-30)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448116
Zepf SHernandez JSchmitt AMinker WPicard R(2020)Driver Emotion Recognition for Intelligent VehiclesACM Computing Surveys10.1145/338879053:3(1-30)Online publication date: 4-Jul-2020
https://dl.acm.org/doi/10.1145/3388790
Wang RYe YJiang CMa X(2019)HIN-VMReSys: Heterogeneous Information Network based Vehicle Music Recommendation System2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)10.1109/ICSIDP47821.2019.9173063(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/ICSIDP47821.2019.9173063
Husain AShiratuddin MWong K(2019)Evaluating the Usability of Browsing Songs by Mood using Visual Texture2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS)10.1109/ICRIIS48246.2019.9073406(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/ICRIIS48246.2019.9073406

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