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Mobile Pervasive Augmented Reality Systems—MPARS: The Role of User Preferences in the Perceived Quality of Experience in Outdoor Applications

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Published:07 February 2020Publication History
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Abstract

After briefly introducing aspects concerning Mobile Augmented Reality Systems, this article delves into the evolution of these systems as pervasive technology. The work debates also on acceptance of this technology in the context of outdoor applications. The need to develop context-aware, close-to-real-time feedback mechanisms that take into consideration a continuous measurement of Quality of Experience is also discussed. For this purpose, the work goes over how to integrate user preferences into context-aware feedback systems, proposing a theoretical model for measuring Quality of Experience. The model is derived from an analysis of previous technology adoption models and incorporates the knowledge of user preferences. This knowledge has been gathered via a public questionnaire.

References

  1. Feng Zhou, Henry Been-Lirn Duh, and Mark Billinghurst. 2008. Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. In Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. IEEE Computer Society, 193--202.Google ScholarGoogle Scholar
  2. Arindam Dey, Mark Billinghurst, Robert W. Lindeman, and J. Swan. 2018. A systematic review of 10 years of augmented reality usability studies: 2005 to 2014. Frontiers in Robotics and AI 5 (2018), 1--28.Google ScholarGoogle ScholarCross RefCross Ref
  3. Alan B. Craig. 2013. Understanding Augmented Reality: Concepts and Applications. Newnes.Google ScholarGoogle Scholar
  4. Bruce Thomas. 2003. Challenges of making outdoor augmented reality games playable. In 2nd CREST Workshop on Advanced Computing and Communicating Techniques for Wearable Information Playing.Google ScholarGoogle Scholar
  5. Mahadev Satyanarayanan, Paramvir Bahl, Ramón Caceres, and Nigel Davies. 2009. The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing 8, 4 (2009).Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Achille Peternier, Xavier Righetti, Mathieu Hopmann, Daniel Thalmann, Matteo Repettoy, George Papagiannakis, Pierre Davy, Mingyu Lim, Nadia Magnenat-Thalmann, Paolo Barsocchi, et al. 2007. Chloe@ university: An indoor, mobile mixed reality guidance system. In Proceedings of the 2007 ACM Symposium on Virtual Reality Software and Technology. ACM, 227--228.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Dimitris Chatzopoulos, Carlos Bermejo, Zhanpeng Huang, and Pan Hui. 2017. Mobile augmented reality survey: From where we are to where we go. IEEE Access (2017).Google ScholarGoogle Scholar
  8. Wenxiao Zhang, Bo Han, and Pan Hui. 2017. On the networking challenges of mobile augmented reality. In Proceedings of the Workshop on Virtual Reality and Augmented Reality Network. ACM, 24--29.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Paula Fraga-Lamas, Tiago M. Fernández-Caramés, Óscar Blanco-Novoa, and Miguel A. Vilar-Montesinos. 2018. A review on industrial augmented reality systems for the industry 4.0 shipyard. IEEE Access 6 (2018), 13358--13375.Google ScholarGoogle ScholarCross RefCross Ref
  10. Tristan Braud, Farshid Hassani Bijarbooneh, Dimitris Chatzopoulos, and Pan Hui. 2017. Future networking challenges: The case of mobile augmented reality. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 1796--1807.Google ScholarGoogle ScholarCross RefCross Ref
  11. Ben D. Sawyer, Victor S. Finomore, Andres A. Calvo, and Peter A. Hancock. 2014. Google glass: A driver distraction cause or cure? Human factors 56, 7 (2014), 1307--1321.Google ScholarGoogle Scholar
  12. Rui Miguel Pascoal and Sérgio Luís Guerreiro. 2017. Information overload in augmented reality: The outdoor sports environments. In Information and Communication Overload in the Digital Age. IGI Global, 271--301.Google ScholarGoogle Scholar
  13. Tobias Höllerer. 2004. User Interfaces for Mobile Augmented Reality Systems. Ph.D. Dissertation. Columbia University New York, NY.Google ScholarGoogle Scholar
  14. George Papagiannakis, Gurminder Singh, and Nadia Magnenat-Thalmann. 2008. A survey of mobile and wireless technologies for augmented reality systems. Computer Animation and Virtual Worlds 19, 1 (2008), 3--22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Fred D. Davis. 1985. A Technology Acceptance Model for Empirically Testing new End-user Information Systems: Theory and Results. Ph.D. Dissertation. Massachusetts Institute of Technology.Google ScholarGoogle Scholar
  16. G. D. M. N. Samaradiwakara and C. G. Gunawardena. 2014. Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal 1, 1 (2014), 21--36.Google ScholarGoogle Scholar
  17. Richard P. Bagozzi, Paul R. Warshaw, et al. 1989. User acceptance of computer technology: A comparison of two theoretical models. Management Science 35, 8 (1989), 982--1003.Google ScholarGoogle ScholarCross RefCross Ref
  18. Viswanath Venkatesh, James Y. L. Thong, and Xin Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. (2012).Google ScholarGoogle Scholar
  19. Borko Furht. 2011. Handbook of Augmented Reality. Springer Science 8 Business Media.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Andreas Möller, Luis Roalter, Stefan Diewald, Johannes Scherr, Matthias Kranz, Nils Hammerla, Patrick Olivier, and Thomas Plötz. 2012. Gymskill: A personal trainer for physical exercises. In Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 213--220.Google ScholarGoogle ScholarCross RefCross Ref
  21. Nicholas D. Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, and Andrew T. Campbell. 2010. A survey of mobile phone sensing. IEEE Communications Magazine 48, 9 (2010).Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Philipp A. Rauschnabel and Young K. Ro. 2016. Augmented reality smart glasses: An investigation of technology acceptance drivers. International Journal of Technology Marketing 11, 2 (2016), 123--148.Google ScholarGoogle ScholarCross RefCross Ref
  23. Chenmeng Wang, Ying He, F. Richard Yu, Qianbin Chen, and Lun Tang. 2017. Integration of networking, caching and computing in wireless systems: A survey, some research issues and challenges. IEEE Communications Surveys 8 Tutorials (2017).Google ScholarGoogle Scholar
  24. Soo Kyun Kim, Shin-Jin Kang, Yoo-Joo Choi, Min-Hyung Choi, and Min Hong. 2017. Augmented-reality survey: From concept to application. KSII Transactions on Internet 8 Information Systems 11, 2 (2017).Google ScholarGoogle Scholar
  25. Mark Billinghurst, Adrian Clark, Gun Lee, et al. 2015. A survey of augmented reality. Foundations and Trends® in Human--Computer Interaction 8, 2--3 (2015), 73--272.Google ScholarGoogle Scholar
  26. Rui Pedro Figueiredo Marques and Joao Carlos Lopes Batista. 2017. Information and Communication Overload in the Digital Age. IGI Global.Google ScholarGoogle Scholar
  27. Akihiko Kitamura, Hiroshi Naito, Takahiko Kimura, Kazumitsu Shinohara, Takashi Sasaki, and Haruhiko Okumura. 2014. Distribution of attention in augmented reality: Comparison between binocular and monocular presentation. IEICE Transactions on Electronics 97, 11 (2014), 1081--1088.Google ScholarGoogle ScholarCross RefCross Ref
  28. Akihiko Kitamura, Hiroshi Naito, Takahiko Kimura, Kazumitsu Shinohara, Takashi Sasaki, and Haruhiko Okumura. 2015. Comparison between binocular and monocular augmented reality presentation in a tracing task. Journal of the Institute of Image Information and Television 69, 10 (2015), J292--J297.Google ScholarGoogle ScholarCross RefCross Ref
  29. Julie Carmigniani, Borko Furht, Marco Anisetti, Paolo Ceravolo, Ernesto Damiani, and Misa Ivkovic. 2011. Augmented reality technologies, systems and applications. Multimedia Tools and Applications 51, 1 (2011), 341--377.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Rute C. Sofia, Saeik Firdose, Luis Amaral Lopes, Waldir Moreira, and Paulo Mendes. 2016. NSense: A people-centric, non-intrusive opportunistic sensing tool for contextualizing social interaction. (2016).Google ScholarGoogle Scholar
  31. Rui Pascoal, Bráulio Alturas, Ana de Almeida, and Rute Sofia. 2018. A survey of augmented reality: Making technology acceptable in outdoor environments. In Proceedings of the 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  32. Ronald T. Azuma. 2016. The most important challenge facing augmented reality. PRESENCE: Teleoperators and Virtual Environments 25, 3 (2016), 234--238.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Ronald T. Azuma. 1999. The challenge of making augmented reality work outdoors. Mixed Reality: Merging Real and Virtual Worlds (1999), 379--390.Google ScholarGoogle ScholarCross RefCross Ref
  34. Jie Yang, Weiyi Yang, Matthias Denecke, and Alex Waibel. 1999. Smart sight: A tourist assistant system. In Proceedings the 3rd International Symposium on Wearable Computers. Digest of Papers. 1999. IEEE, 73--78.Google ScholarGoogle ScholarCross RefCross Ref
  35. Matthias Kranz, Andreas Möller, Nils Hammerla, Stefan Diewald, Thomas Plötz, Patrick Olivier, and Luis Roalter. 2013. The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive and Mobile Computing 9, 2 (2013), 203--215.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Tim Althoff, Ryen W. White, and Eric Horvitz. 2016. Influence of Pokémon Go on physical activity: Study and implications. Journal of Medical Internet Research 18, 12 (2016).Google ScholarGoogle ScholarCross RefCross Ref
  37. Luca Vacchetti, Vincent Lepetit, Michal Ponder, George Papagiannakis, Pascal Fua, Daniel Thalmann, and Nadia Magnenat Thalmann. 2004. A stable real-time AR framework for training and planning in industrial environments. In Virtual and Augmented Reality Applications in Manufacturing. Springer, 129--145.Google ScholarGoogle Scholar
  38. Ozlem Durmaz Incel, Mustafa Kose, and Cem Ersoy. 2013. A review and taxonomy of activity recognition on mobile phones. BioNanoScience 3, 2 (2013), 145--171.Google ScholarGoogle ScholarCross RefCross Ref

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