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Quantifying Museum Visitor Attention Using Bluetooth Proximity Beacons

Part of the Communications in Computer and Information Science book series (CCIS,volume 1226)

Abstract

This paper shows initial work on utilizing Bluetooth Low-Energy (BLE) beacons to uncover patterns in visitor paths and to determine the concentration of visitor attention within the Ateneo Art Gallery (AAG), the Philippines’ first museum of modern art. Participants carried phones instrumented with an application that logged distance estimates from BLE beacons deployed around the museum. The logs were then analyzed to produce scarf plots that visually represent paths taken. The results show the potential of BLE beacons for indoor location tracking. Aggregated visualization of similar scarf plots showed four notable patterns that provide insights on the museum areas that draw attention. The assumed usual route (pattern1: Start at 1st floor, go to 2nd floor then visit 3rd floor) was confirmed and an uncommon visiting pattern was discovered (pattern 2: Start at 3rd floor, go to 2nd floor, then visit 1st floor). It was also found that some visitors do not get to explore the entire museum (pattern 3: Some visitors only went to the 1st and 2nd floors. Pattern 4: Some visitors only went to the 2nd and 3rd floors). These insights can be used to make decisions regarding exhibit arrangement or museum layout design.

Keywords

  • Museum visitorship
  • BLE beacons
  • Indoor location tracking

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  • DOI: 10.1007/978-3-030-50732-9_36
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References

  1. Martella, C., Miraglia, A., Cattani, M., Van Steen, M.: Leveraging proximity sensing to mine the behavior of museum visitors. In: 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–9. IEEE (2016)

    Google Scholar 

  2. Yoshimura, Y., Krebs, A., Ratti, C.: Noninvasive bluetooth monitoring of visitors’ length of stay at the louvre. IEEE Pervasive Comput. 16(2), 26–34 (2017)

    CrossRef  Google Scholar 

  3. Rashed, M.G., Suzuki, R., Yonezawa, T., Lam, A., Kobayashi, Y., Kuno, Y.: Tracking visitors in a real museum for behavioral analysis. In: 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), pp. 80–85. IEEE (2016)

    Google Scholar 

  4. Chang, Y.J., Wang, T.Y.: Comparing picture and video prompting in autonomous indoor wayfinding for individuals with cognitive impairments. Pers. Ubiquit. Comput. 14(8), 737–747 (2010)

    CrossRef  Google Scholar 

  5. Chang, Y.J., Chen, Y.R., Chang, C.Y., Wang, T.Y.: Video prompting and indoor wayfinding based on bluetooth beacons: a case study in supported employment for people with severe mental illness. In: 2009 WRI International Conference on Communications and Mobile Computing, vol. 3, pp. 137–141. IEEE (2009)

    Google Scholar 

  6. Taheri, B., Jafari, A., O’Gorman, K.: Keeping your audience: presenting a visitor engagement scale. Tour. Manag. 42, 321–329 (2014)

    CrossRef  Google Scholar 

  7. Black, G.: Transforming Museums in the Twenty-First Century. Routledge, Abingdon (2012)

    CrossRef  Google Scholar 

  8. Chamberlin, M.: A study of young adult programming at american historic house museums. Doctoral dissertation, The University of the Arts (2018)

    Google Scholar 

  9. Ali, S., Koleva, B., Bedwell, B., Benford, S.: Deepening visitor engagement with museum exhibits through hand-crafted visual markers. In: Proceedings of the 2018 Designing Interactive Systems Conference, pp. 523–534 (2018)

    Google Scholar 

  10. Rashed, M.G., Suzuki, R., Yonezawa, T., Lam, A., Kobayashi, Y., Kuno, Y.: Robustly tracking people with lidars in a crowded museum for behavioral analysis. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100(11), 2458–2469 (2017)

    CrossRef  Google Scholar 

  11. Cesário, V., Radeta, M., Matos, S., Nisi, V.: The ocean game: assessing children’s engagement and learning in a museum setting using a treasure-hunt game. In: Extended Abstracts Publication of the Annual Symposium on Computer-Human Interaction in Play, pp. 99–109 (2017)

    Google Scholar 

  12. Jeon, K.E., She, J., Soonsawad, P., Ng, P.C.: Ble beacons for internet of things applications: survey, challenges, and opportunities. IEEE Internet Things J. 5(2), 811–828 (2018)

    CrossRef  Google Scholar 

  13. Vavoula, G., Tseliou, M.A., Tsiviltidou, Z.: Bluetooth low energy beacon-based positioning for multimedia guides in heritage buildings: a case study. In: World Conference on Mobile and Contextual Learning, pp. 102–109 (2019)

    Google Scholar 

  14. Spachos, P., Plataniotis, K.N.: BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum (2020). arXiv preprint arXiv:2001.07686

  15. Vidal Jr., E.C.E., Ty, J.F., Caluya, N.R., Rodrigo, M.M.T.: MAGIS: mobile augmented-reality games for instructional support. Interact. Learn. Environ. 27(7), 895–907 (2019)

    CrossRef  Google Scholar 

  16. Ng, P.C., She, J., Park, S.: Notify-and-interact: a beacon-smartphone interaction for user engagement in galleries. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 1069–1074. IEEE (2017)

    Google Scholar 

  17. Pitman, T.: BluetoothLE for iOS, tvOS and Android, Shaltamic LLC (2014). Unity Asset Store https://assetstore.unity.com/packages/tools/network/bluetooth-le-for-ios-tvos-and-android-26661

  18. Al Qathrady, M., Helmy, A.: Improving BLE distance estimation and classification using TX power and machine learning: a comparative analysis. In: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, pp. 79–83 (2017)

    Google Scholar 

  19. Iqbal, Z., et al.: Accurate real time localization tracking in a clinical environment using bluetooth low energy and deep learning. PloS one 13(10), e0205392 (2018)

    MathSciNet  CrossRef  Google Scholar 

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Acknowledgements

We thank the Ateneo de Manila University Loyola Schools for the grant entitled “Ateneo Art Gallery Visitor Tracking Study Using Bluetooth Low-Energy Beacons”. We also wish to express our thanks to the Ateneo Art Gallery Museum Education Office and its Administrators, specifically Ms. Estela Bagos (AAG Museum Education Officer) and Ms. Victoria Herrera (AAG Director and Chief Curator) for allowing the conduct of this experiment within the museum. Lastly, we thank the participants who willingly volunteered to participate in the study.

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Correspondence to Jonathan D. L. Casano .

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Casano, J.D.L., Agapito, J.L., Moreno, A., Rodrigo, M.M.T. (2020). Quantifying Museum Visitor Attention Using Bluetooth Proximity Beacons. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-50732-9_36

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  • DOI: https://doi.org/10.1007/978-3-030-50732-9_36

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