A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform

  • N. Hernández
  • B. Arnrich
  • J. Favela
  • C. Ersoy
  • B. Demiray
  • J. Fontecha
Original Research
  • 89 Downloads

Abstract

Walking is a fundamental part of a physically active lifestyle, it is one of everyday activities that positively impacts health and wellbeing. In this paper we describe the challenges and experiences of conducting a sensing campaign in the wild. We make use of mk-sense; a software platform to facilitate the deployment of collaborative sensing campaigns. We elaborate on two cross-cultural studies conducted in four different countries (Mexico, Turkey, Spain, and Switzerland) with a total of 77 participants. We present a detailed description of the data collected from one of the studies aimed at measuring walkability around three different university campuses. The analysis of the data shows that walkability can be assessed using information from the sensors in the smartphones and results from surveys answered by participants. In addition, we analyze issues about data sharing and privacy awareness.

Keywords

Smartphone Sensing campaign Data sharing Privacy concern Completeness data 

Notes

Acknowledgements

We thank the participants of the two sensing campaigns described in the paper for contributing their time and effort in making them successful. This work was partially funded by (1) the Co-Funded Brain Circulation Scheme Project “Pervasive Healthcare: Towards Computational Networked Life Science” (TÜBITAK Co-Circ 2236, Grant agreement number: 112C005) supported by TÜBİTAK and EC FP7 Marie Curie Action COFUND and (2) the EC FP7 Marie Curie Action “UBIHEALTH-Exchange of Excellence in Ubiquitous Computing Technologies to Address Healthcare Challenges” (Project number 316337).

References

  1. Abdelzaher T, Anokwa Y, Boda P et al (2007) Mobiscopes for human spaces. IEEE Pervasive Comput 6:20–29.  https://doi.org/10.1109/MPRV.2007.38 CrossRefGoogle Scholar
  2. Bengtsson L, Lu X, Thorson A et al (2011) Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in haiti. PLoS Med.  https://doi.org/10.1371/journal.pmed.1001083 Google Scholar
  3. Berke EM, Choudhury T, Ali S, Rabbi M (2011) Objective measurement of sociability and activity: mobile sensing in the community. Ann Fam Med 9:344–350.  https://doi.org/10.1370/afm.1266 CrossRefGoogle Scholar
  4. Berkeley J (2015) Planet of the phones. Economics.  https://doi.org/10.1001/archopht.122.4.666 Google Scholar
  5. Brownstein JS, Freifeld CC, Madoff LC (2009) Digital disease detection-harnessing the Web for public health surveillance. N Engl J Med 360:2153–2157.  https://doi.org/10.1056/NEJMp1002530 CrossRefGoogle Scholar
  6. Campbell AT, Eisenman SB, Lane ND et al (2008) The rise of people-centric sensing. IEEE Internet Comput 12:12–21.  https://doi.org/10.1109/MIC.2008.90 CrossRefGoogle Scholar
  7. Chen PY, Cheng SM, Ting PS et al (2015) When crowdsourcing meets mobile sensing: a social network perspective. IEEE Commun Mag 53:157–163.  https://doi.org/10.1109/MCOM.2015.7295478 CrossRefGoogle Scholar
  8. Christian HE, Bull FC, Middleton NJ et al (2011) How important is the land use mix measure in understanding walking behaviour? Results from the RESIDE study. Int J Behav Nutr Phys Act 8:55.  https://doi.org/10.1186/1479-5868-8-55 CrossRefGoogle Scholar
  9. Cornelius C, Kapadia A, Kotz D et al (2008) Anonysense: privacy-aware people-centric sensing. In: Mobisys08-Proceeding of the 6th international conference on mobile systems, applications, and services.  https://doi.org/10.1145/1378600.1378624
  10. Corno F, De Russis L, Montanaro T (2017) Estimate user meaningful places through low-energy mobile sensing. In: 2016 IEEE international conference on systems, man, and cybernetics, SMC 2016—conference proceedings, pp 3039–3044Google Scholar
  11. Das T, Mohan P, Padmanabhan VN et al (2010) PRISM: platform for remote sensing using smartphones. In: Proceedings of 8th international conference on Mobile systems, applications, and services-MobiSys’, vol 10, pp 63–76.  https://doi.org/10.1145/1814433.1814442
  12. Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquitous Comput 10:255–268.  https://doi.org/10.1007/s00779-005-0046-3 CrossRefGoogle Scholar
  13. Froehlich J, Chen MY, Consolvo S et al (2007) MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In: Proceedings of 5th international conference on Mobile systems, applications, and services San Juan, pp 57–70.  https://doi.org/10.1145/1247660.1247670
  14. Harari GM, Lane ND, Wang R et al (2016) Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect Psychol Sci 11:838–854.  https://doi.org/10.1177/1745691616650285 CrossRefGoogle Scholar
  15. Hernández N, Yavuz G, Eşrefoğlu R et al (2015) Thought and life logging: a pilot study. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). pp 26–36Google Scholar
  16. Kanhere SS (2013) Participatory sensing: crowdsourcing data from mobile smartphones in urban spaces. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 19–26Google Scholar
  17. Kim S, Mankoff J, Paulos E (2013) Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science. In: Proceedings of the 2013 conference on, pp 1453–1462Google Scholar
  18. Lu H, Lane ND, Eisenman SB, Campbell AT (2010) Bubble-sensing: binding sensing tasks to the physical world. Pervasive Mob Comput 6:58–71.  https://doi.org/10.1016/j.pmcj.2009.10.005 CrossRefGoogle Scholar
  19. Macias E, Suarez A, Lloret J (2013) Mobile sensing systems. Sensors 13:17292–17321.  https://doi.org/10.3390/s131217292 CrossRefGoogle Scholar
  20. Moraes ALD, Fonseca F, Esteves MGP et al (2014) A meta-model for crowdsourcing platforms in data collection and participatory sensing. In: Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design, CSCWD 2014, pp 429–434Google Scholar
  21. Nandugudi A, Maiti A, Ki T et al (2013) {PhoneLab}: a large programmable smartphone testbed. In: Proceedings of first international work on sensors big data mining, pp 1–6.  https://doi.org/10.1145/2536714.2536718
  22. Pasupathi M, Carstensen LL (2003) Age and emotional experience during mutual reminiscing. Psychol Aging 18:430–442.  https://doi.org/10.1037/0882-7974.18.3.430 CrossRefGoogle Scholar
  23. Perdue WC, Stone LA, Gostin LO (2003) The built environment and its relationship to the public’s health: the legal framework. Am J Public Health 93:1390–1394CrossRefGoogle Scholar
  24. Quercia D, Aiello LM, Schifanella R, Davies A (2015) The digital life of walkable streets.  https://doi.org/10.1145/2736277.2741631
  25. Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8:10–17CrossRefGoogle Scholar
  26. Silva TH, Vaz de Melo POS, Almeida JM et al (2014) Revealing the city that we cannot see. ACM Trans Internet Technol 14:1–23.  https://doi.org/10.1145/2677208 CrossRefGoogle Scholar
  27. Suddendorf T, Corballis MC (2007) The evolution of foresight: what is mental time travel, and is it unique to humans?. Behav Brain Sci.  https://doi.org/10.1017/S0140525X07001975 Google Scholar
  28. Tentori M, Favela J, González V (2006) Quality of privacy (QoP) for the design of ubiquitous healthcare applications. J Univ Comput Sci 12:252–269Google Scholar
  29. Villanueva K, Pereira G, Knuiman M et al (2013) The impact of the built environment on health across the life course: design of a cross-sectional data linkage study. BMJ Open 3:e002482.  https://doi.org/10.1136/bmjopen-2012-002482 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of ComputingUlster UniversityNewtownabbeyUK
  2. 2.Computer Science DepartmentCICESE Research CenterEnsenadaMexico
  3. 3.Computer Engineering DepartmentBoğaziçi UniversityIstanbulTurkey
  4. 4.Psychology DepartmentUniversity of ZürichZurichSwitzerland
  5. 5.MAmI Research LaboratoryUniversity of Castilla-La ManchaCiudad RealSpain

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