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Addressing the Challenges of COVID-19 Social Distancing Through Passive Wi-Fi and Ubiquitous Analytics: A Real World Deployment

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12933))

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Abstract

During the COVID-19 pandemic, social distancing measures were employed to contain its spread. This paper describes the deployment and testing of a passive Wi-Fi scanning system to help people keep track of crowded spaces, hence comply with social distancing measures. The system is based on passive Wi-Fi sensing to detect human presence in 93 locations around a medium-sized European Touristic Island. This data is then used in website plugins and a mobile application to inform citizens and tourists about the locations’ crowdedness with real-time and historical data. To understand how people react to this type of information, we deployed online questionnaires in situ to collect user insights regarding the usefulness, safety, and privacy concerns. Results show that users considered the occupancy data reported by the system as positively related to their perception. Furthermore, the public display of this data made them feel safer while travelling and planning their commute.

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References

  1. Ahmed, A.M.: Designing a framework to control the spread of COVID-19 by utilizing cellular system. Kurdistan J. Appl. Res. 5, 146–153 (2020). https://doi.org/10.24017/covid.16

    Article  Google Scholar 

  2. Alsaeedy, A.A.R., Chong, E.K.P.: Detecting regions at risk for spreading COVID-19 using existing cellular wireless network functionalities. IEEE Open J. Eng. Med. Biol. 1, 187–189 (2020). https://doi.org/10.1109/OJEMB.2020.3002447. Conference Name: IEEE Open Journal of Engineering in Medicine and Biology

  3. Baniukevic, A., Jensen, C., Lu, H.: Hybrid indoor positioning with Wi-Fi and bluetooth: architecture and performance. In: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, pp. 207–216 (2013). https://doi.org/10.1109/MDM.2013.30

  4. Böhmer, M., Hecht, B., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with angry birds, Facebook and kindle: a large scale study on mobile application usage. Presented at the (2011)

    Google Scholar 

  5. Bonné, B., Barzan, A., Quax, P., Lamotte, W.: WiFiPi: involuntary tracking of visitors at mass events. Presented at the (2013). https://doi.org/10.1109/WoWMoM.2013.6583443

  6. Brignull, H., Rogers, Y.: Enticing people to interact with large public displays in public spaces. Interact 3, 17–24 (2003)

    Google Scholar 

  7. Buettner, M., Prasad, R., Philipose, M., Wetherall, D.: Recognizing daily activities with RFID-based sensors. Presented at the (2009). https://doi.org/10.1145/1620545.1620553

  8. Diethei, D., Niess, J., Stellmacher, C., Stefanidi, E., Schöning, J.: Sharing heartbeats: motivations of citizen scientists in times of crises. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3411764.3445665

  9. Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1

    Chapter  MATH  Google Scholar 

  10. Fatih, Şİ, Gökhan, A.N., Panić, S., Stefanović, Č, Yağanoğlu, M., Prilinčević, B.: Covid-19 risk assessment in public transport using ambient sensor data and wireless communications. Bull. Nat. Sci. Res. 10(2), 43–50 (2020). https://doi.org/10.5937/bnsr10-29239

    Article  Google Scholar 

  11. Florez, H., Singh, S.: Online dashboard and data analysis approach for assessing COVID-19 case and death data. F1000Research, vol. 9, no. 570, p. 10. 2020.12688/f1000research.24164.1

    Google Scholar 

  12. Fuentes, C., Rodríguez, I., Herskovic, V.: EmoBall: a study on a tangible interface to self-report emotional information considering digital competences. In: Bravo, J., Hervás, R., Villarreal, V. (eds.) AmIHEALTH 2015. LNCS, vol. 9456, pp. 189–200. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26508-7_19

    Chapter  Google Scholar 

  13. Gallacher, S., Golsteijn, C., Rogers, Y., Capra, L., Eustace, S.: SmallTalk: using tangible interactions to gather feedback from children. Presented at the (2016). https://doi.org/10.1145/2839462.2839481

  14. Gallacher, S., et al.: Mood squeezer: lightening up the workplace through playful and lightweight interactions. Presented at the (2015). https://doi.org/10.1145/2675133.2675170

  15. Gao, C., Li, P., Zhang, Y., Liu, J., Wang, L.: People counting based on head detection combining Adaboost and CNN in crowded surveillance environment. Neurocomputing 208, 108–116 (2016). https://doi.org/10.1016/j.neucom.2016.01.097

    Article  Google Scholar 

  16. Golsteijn, C., et al.: VoxBox: a tangible machine that gathers opinions from the public at events. Presented at the (2015). https://doi.org/10.1145/2677199.2680588

  17. Heimerl, K., Gawalt, B., Chen, K., Parikh, T., Hartmann, B.: CommunitySourcing: engaging local crowds to perform expert work via physical kiosks. Presented at the (2012). https://doi.org/10.1145/2207676.2208619

  18. Houben, S., et al.: Roam-IO: engaging with people tracking data through an interactive physical data installation. In: Proceedings of the 2019 on Designing Interactive Systems Conference (DIS 2019), pp. 1157–1169. Association for Computing Machinery (2019). https://doi.org/10.1145/3322276.3322303

  19. Houben, S., Weichel, C.: Overcoming interaction blindness through curiosity objects. In: CHI ’13 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2013), pp. 1539–1544. Association for Computing Machinery (2013). https://doi.org/10.1145/2468356.2468631

  20. Ju, W., Sirkin, D.: Animate objects: how physical motion encourages public interaction. In: Ploug, T., Hasle, P., Oinas-Kukkonen, H. (eds.) PERSUASIVE 2010. LNCS, vol. 6137, pp. 40–51. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13226-1_6

    Chapter  Google Scholar 

  21. Kjærgaard, M.B., Wirz, M., Roggen, D., Tröster, G.: Mobile sensing of pedestrian flocks in indoor environments using WiFi signals. Presented at the (2012). https://doi.org/10.1109/PerCom.2012.6199854

  22. Koch, M., von Luck, K., Schwarzer, J., Draheim, S.: The novelty effect in large display deployments-experiences and lessons-learned for evaluating prototypes. In: Proceedings of 16th European Conference on Computer-Supported Cooperative Work-Exploratory Papers. European Society for Socially Embedded Technologies (EUSSET) (2018). https://doi.org/10.18420/ecscw2018_3

  23. Koehlmoos, T.P., Janvrin, M.L., Korona-Bailey, J., Madsen, C., Sturdivant, R.: COVID-19 self-reported symptom tracking programs in the united states: framework synthesis. J. Med. Internet Res. 22(10), e23297 (2020). https://doi.org/10.2196/23297

    Article  Google Scholar 

  24. Koeman, L., Kalnikaité, V., Rogers, Y.: “Everyone is talking about it!”: a distributed approach to urban voting technology and visualisations. Presented at the (2015). https://doi.org/10.1145/2702123.2702263

  25. Li, F., Valero, M., Shahriar, H., Khan, R.A., Ahamed, S.I.: Wi-COVID: a COVID-19 symptom detection and patient monitoring framework using WiFi. Smart Health 19, 100147 (2021). https://doi.org/10.1016/j.smhl.2020.100147

    Article  Google Scholar 

  26. Meneses, F., Moreira, A.: Large scale movement analysis from WiFi based location data. Presented at the (2012). https://doi.org/10.1109/IPIN.2012.6418885

  27. Menni, C., et al.: Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 26(7), 1037–1040 (2020). https://doi.org/10.1038/s41591-020-0916-2

    Article  Google Scholar 

  28. Müller, J., Alt, F., Michelis, D., Schmidt, A.: Requirements and design space for interactive public displays. Presented at the (2010). https://doi.org/10.1145/1873951.1874203

  29. Nunes, N., Ribeiro, M., Prandi, C., Nisi, V.: Beanstalk: a community based passive Wi-Fi tracking system for analysing tourism dynamics. Presented at the (2017). https://doi.org/10.1145/3102113.3102142

  30. Oswald, M., Grace, J.: The COVID-19 contact tracing app in England and ‘experimental proportionality’. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3632870

  31. Park, Y.J., et al.: COVID-19 national emergency response center, epidemiology and case management team: contact tracing during coronavirus disease outbreak, South Korea, 2020. Emerg. Infect. Dis. 26(10), 2465–2468 (2020). https://doi.org/10.3201/eid2610.201315

    Article  Google Scholar 

  32. Prandi, C., Nisi, V., Ribeiro, M., Nunes, N.: Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience. J. Big Data 8(1), 1–25 (2021). https://doi.org/10.1186/s40537-021-00442-w

    Article  Google Scholar 

  33. Ram, N., Gray, D.: Mass surveillance in the age of COVID-19. J. Law Biosci. 7, lsaa023 (2020). https://doi.org/10.1093/jlb/lsaa023

    Article  Google Scholar 

  34. Redin, D., Vilela, D., Nunes, N., Ribeiro, M., Prandi, C.: ViTFlow: a platform to visualize tourists flows in a rich interactive map-based interface, pp. 1–2. IEEE (2017). https://doi.org/10.23919/SustainIT.2017.8379814

  35. Ribeiro, M., Nisi, V., Prandi, C., Nunes, N.: A data visualization interactive exploration of human mobility data during the COVID-19 outbreak: a case study, pp. 1–6. IEEE (2020). https://doi.org/10.1109/ISCC50000.2020.9219552

  36. Ribeiro, M., Nunes, N., Nisi, V., Schöning, J.: Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies. Pers. Ubiquit. Comput., 1–15 (2020). https://doi.org/10.1007/s00779-020-01441-z

  37. Ruiz-Ruiz, A.J., Blunck, H., Prentow, T.S., Stisen, A., Kjærgaard, M.B.: Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning. Presented at the (2014). https://doi.org/10.1109/PerCom.2014.6813953

  38. Said, M., Samuel, M., Shannan, N., Bashir, F.M., Dodo, Y.: Novel vision-based thermal people counting tool for tracking infected people with viruses like COVID-19. J. Adv. Res. Dyn. Control Syst. 12, 1115–1119 (2020). https://doi.org/10.5373/JARDCS/V12SP7/20202210

    Article  Google Scholar 

  39. Shaw, P., Mikusz, M., Nurmi, P., Davies, N.: Tacita: a privacy preserving public display personalisation service. Presented at the (2018). https://doi.org/10.1145/3267305.3267627

  40. Sohn, T., et al.: Mobility detection using everyday GSM traces. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 212–224. Springer, Heidelberg (2006). https://doi.org/10.1007/11853565_13

    Chapter  Google Scholar 

  41. Stevens, H., Haines, M.B.: TraceTogether: pandemic response, democracy, and technology. East Asian Sci. Technol. Soc. 14(3), 523–532 (2020). https://doi.org/10.1215/18752160-8698301

    Article  Google Scholar 

  42. Tang, X., Xiao, B., Li, K.: Indoor crowd density estimation through mobile smartphone Wi-Fi probes. IEEE Trans. Syst. Man Cybern. Syst. 50(7), 2638–2649 (2020). https://doi.org/10.1109/TSMC.2018.2824903. Conference Name: IEEE Transactions on Systems, Man, and Cybernetics: Systems

  43. Vedaei, S.S., et al.: COVID-SAFE: an IoT-based system for automated health monitoring and surveillance in post-pandemic life. IEEE Access 8, 188538–188551 (2020).https://doi.org/10.1109/ACCESS.2020.3030194. Conference Name: IEEE Access

  44. Whitelaw, S., Mamas, M.A., Topol, E., Spall, H.G.C.V.: Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digital Health 2(8), e435–e440 (2020). https://doi.org/10.1016/S2589-7500(20)30142-4

    Article  Google Scholar 

  45. Whittle, J., et al.: VoiceYourView: collecting real-time feedback on the design of public spaces. Presented at the (2010). https://doi.org/10.1145/1864349.1864358

  46. Wissel, B.D., et al.: An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time. J. Am. Med. Inform. Assoc. 27(7), 1121–1125 (2020). https://doi.org/10.1093/jamia/ocaa071

    Article  Google Scholar 

  47. Zhao, X., Delleandrea, E., Chen, L.: A people counting system based on face detection and tracking in a video, pp. 67–72. IEEE (2009). https://doi.org/10.1109/AVSS.2009.45

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Correspondence to Miguel Ribeiro .

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Ribeiro, M., Nunes, N., Ferreira, M., Nogueira, J., Schöning, J., Nisi, V. (2021). Addressing the Challenges of COVID-19 Social Distancing Through Passive Wi-Fi and Ubiquitous Analytics: A Real World Deployment. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12933. Springer, Cham. https://doi.org/10.1007/978-3-030-85616-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-85616-8_1

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