Personal and Ubiquitous Computing

, Volume 21, Issue 4, pp 761–773 | Cite as

Environmental exposure assessment using indoor/outdoor detection on smartphones

  • Theodoros Anagnostopoulos
  • Juan Camilo Garcia
  • Jorge Goncalves
  • Denzil Ferreira
  • Simo Hosio
  • Vassilis Kostakos
Original Article

Abstract

We present an energy-efficient method for Indoor/Outdoor detection on smartphones. The creation of an accurate environmental exposure detection method enables crucial advances to a number of health sciences, which seek to model patients’ environmental exposure. In a field trial, we collected data from multiple smartphone sensors, along with explicit indoor/outdoor labels entered by participants. Using this rich dataset, we evaluate multiple classification models, optimised for accuracy and low energy consumption. Using all sensors, we can achieve 99% classification accuracy. Using only a subset of energy-efficient sensors we achieve 92.91% accuracy. We systematically quantify how subsampling can be used as a trade-off for accuracy and energy consumption. Our work enables researchers to quantify environmental exposure using commodity smartphones.

Keywords

Energy efficiency Environmental exposure Indoor/outdoor detection Smartphones 

References

  1. 1.
    Almanac for Computers, 1990. Nautical Almanac Office, US Naval Observatory, Washington, D.C.Google Scholar
  2. 2.
  3. 3.
    Adgate JL, Church TR, Ryan AD, Ramachandran G et al (2004) Outdoor, indoor, and personal exposure to VOCs in children. Environ Health Perspect:1386–1392Google Scholar
  4. 4.
    Ferreira D, Kostakos V, Dey AK (2015) AWARE: mobile context instrumentation framework. Frontiers in ICT 2(6):1–9. doi:10.3389/fict.2015.00006 Google Scholar
  5. 5.
    Azizyan M, Constandache I, Choudhury RR (2009) Surround sense: mobile phone localization via ambience fingerprinting. In: In Proceedings of the 15th International Conference on Mobile Computing and Networking, ACM, pp 261–272. doi:10.1145/1614320.1614350 Google Scholar
  6. 6.
    Baghurst PA, McMichael AJ, Wigg NR, Vimpani GV, Robertson EF, Roberts RJ, Tong S-L (1992) Environmental exposure to lead and Children's intelligence at the age of seven years. N Engl J med 327(18):1279–1284. doi:10.1056/nejm199210293271805 CrossRefGoogle Scholar
  7. 7.
    Barnes J, Rizos C, Wang J, Small D, Voigt G, Gambale N (2002) High precision indoor and outdoor positioning using LocataNet. Positioning 1:05Google Scholar
  8. 8.
    Fehmi Ben Abdesslem, Andrew Phillips and Tristan Henderson. 2009. Less is More: Energy-efficient Mobile Sensing with Senseless. In Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds, 61–62. doi:10.1145/1592606.1592621.
  9. 9.
    Berkovich G (2014) Accurate and reliable real-time indoor positioning on commercial smartphones. In: In International Conference on Indoor Positioning and Indoor Navigation, IEEE, pp 670–677. doi:10.1109/IPIN.2014.7275542 Google Scholar
  10. 10.
    Bill R, Cap C, Kofahl M, Mundt T (2004) Indoor and outdoor positioning in mobile environmentsa review and some investigations on wlan-positioning. Geographic Information Sciences 10:2Google Scholar
  11. 11.
    Chen K-Y, Harniss M, Lim J, Han Y, Johnson K, Patel S (2013) uLocate: a Ubiquitous location tracking system for people aging with disabilities. In: In Proceedings of the International Conference on Body Area Networks, pp 173–176. doi:10.4108/icst.bodynets.2013.253584 Google Scholar
  12. 12.
    Cho S-B (2015) Exploiting machine learning techniques for location recognition and prediction with smartphone logs. Neurocomputing 176(C):98–106. doi:10.1016/j.neucom.2015.02.079 Google Scholar
  13. 13.
    Dey AK, Wac K, Ferreira D, Tassini K, Hong J-H, Ramos J (2011) Getting closer: an empirical investigation of the proximity of user to their smart phones. In: In International Conference on Ubiquitous Computing, ACM, pp 163–172. doi:10.1145/2030112.2030135 Google Scholar
  14. 14.
    Do T, Dousse O, Miettinen M, Gatica-Perez D (2015) A probabilistic kernel method for human mobility prediction with smartphones. Pervasive and Mobile Computing 20:13–28. doi:10.1016/j.pmcj.2014.09.001 CrossRefGoogle Scholar
  15. 15.
    Gani MO, Casey O'B, Ahamed SI, Smith RO (2013) RSSI based indoor localization for smartphone using fixed and mobile wireless node. In Computer Software and Applications Conference, IEEE, pp 110–117. doi:10.1109/COMPSAC.2013.18
  16. 16.
    M. I. Gilmour, Maritta S. Jaakkola, Stephanie J. London, Andre A. E. Nel and Christine C. A. Rogers. 2006. How exposure to environmental tobacco smoke, outdoor air pollutants, and increased pollen burdens influences the incidence of asthma. Environmental Health Perspectives, 627–633.Google Scholar
  17. 17.
    Goncalves J, Sarsenbayeva Z, van Berkel N, Luo C, Hosio S, Risanen S, Rintamäki H, Kostakos V (2017) Tapping task performance on smartphones in cold temperature. Interact Comput 29(3):355–367Google Scholar
  18. 18.
    Howdeshell KL, Hotchkiss AK, Thayer KA, Vandenbergh JG, Vom FS, Saal (1999) Environmental toxins: exposure to bisphenol a advances puberty. Nature 401(6755):763–764CrossRefGoogle Scholar
  19. 19.
    Simon Klakegg, Jorge Goncalves, Niels van Berkel, Chu Luo, Simo Hosio and Vassilis Kostakos. 2017. Towards commoditised near infrared spectroscopy. In Proceedings of the ACM SIGCHI Conference on Designing Interactive Systems, to appear.Google Scholar
  20. 20.
    Leu J-S, Yu M-C, Tzeng H-J (2015) Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-fi signals. Comput Netw 91:329–340. doi:10.1016/j.comnet.2015.08.032 CrossRefGoogle Scholar
  21. 21.
    Mo Li, Pengfei Zhou, Yuanqing Zheng, Zhenjiang Li and Guobin Shen. 2014. IODetector: a generic Service for Indoor/outdoor detection. ACM trans. Sen. Netw 11, 2, 28:1-28:29. doi:10.1145/2659466.
  22. 22.
  23. 23.
    A. Lindo, Maria del Carmen Perez, J. Urena, David Gualda, Eloy Garcia and J. M. Villadangos. 2014. Ultrasonic signal acquisition module for smartphone indoor positioning. Emerging Technology and Factor Automation, 1–4.Google Scholar
  24. 24.
    Guangwen Liu, Masayuki Iwai, Yoshito Tobe, Dunstan Matekenya, Khan Hossain, Masaki Ito and Kaoru Sezaki. 2014. Beyond horizontal location context: measuring elevation using Smartphone's barometer. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, ACM, 459-468. doi:10.1145/2638728.2641670.
  25. 25.
    Lopes SI, Vieira JM, Reis J, Albuquerque D, Carvalho NB (2015) Accurate smartphone indoor positioning using a WSN infrastructure and non-invasive audio for TDoA estimation. Pervasive and Mobile Computing 20:29–46. doi:10.1016/j.pmcj.2014.09.003 CrossRefGoogle Scholar
  26. 26.
    Weiwei Jiang, Denzil Ferreira, Jani Ylioja, Jorge Goncalves and Vassilis Kostakos. 2014. Pulse: low bitrate wireless magnetic communication for smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 261-265.Google Scholar
  27. 27.
    Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury and Andrew T. Campbell. 2010. The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM, 71-84. doi:10.1145/1869983.1869992.
  28. 28.
    Hiroshi Mizuno, Ken Sasaki and Hiroshi Hosaka. 2007. Indoor-outdoor Positioning and Lifelog Experiment with Mobile Phones. In Proceedings of the 2007 Workshop on multimodal interfaces in semantic Interaction, ACM, 55–57. doi:10.1145/1330572.1330582.
  29. 29.
    Monn C (2001) Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone. Atmos Environ 35(1):1–32. doi:10.1016/s1352-2310(00)00330-7 CrossRefGoogle Scholar
  30. 30.
    Namineni PK, Davey T, Siebert G, Jacobus CJ (2010) Wireless mobile indoor/outdoor tracking system. Patent US 7852262:B2Google Scholar
  31. 31.
    Lionel Ni, Yunhao Liu, Yiu C. Lau and Abhishek A. P. Patil. 2004. LANDMARC: indoor location sensing using active RFID. Wirel Netw 10, 6, 701–710.Google Scholar
  32. 32.
    Masayuki Okamoto and Cheng Chen. 2015. Improving GPS-based indoor-outdoor detection with moving direction information from smartphone. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 257-260. doi:10.1145/2800835.2800939.
  33. 33.
    O'Neill E, Vassilis K, Kindberg T, Schiek A, Penn A, Fraser D, Jones T (2006) Instrumenting the city: developing methods for observing and understanding the digital cityscape. In: In International Conference on Ubiquitous Computing, springer, pp 315–332. doi:10.1007/11853565_19 Google Scholar
  34. 34.
    Kazushige Ouchi and Miwako Doi. 2012. Indoor-outdoor Activity Recognition by a Smartphone. In Proceedings of the 2012 ACM conference on Ubiquitous computing, ACM, 537–537. doi:10.1145/2370216.2370297.
  35. 35.
    Patandin S, Koopman-Esseboom C, De Ridder M, Weisglas-Kuperus N, Sauer P (1998) Effects of environmental exposure to polychlorinated biphenyls and dioxins on birth size and growth in Dutch children. Pediatr res 44(4):538–545. doi:10.1203/00006450-199810000-00012 CrossRefGoogle Scholar
  36. 36.
    Patandin S, Lanting CI, Mulder PG, Rudy Boersma E, Sauer PJ, Weisglas-Kuperus N (1999) Effects of environmental exposure to polychlorinated biphenyls and dioxins on cognitive abilities in Dutch children at 42 months of age. J Pediatr 134(1):33–41CrossRefGoogle Scholar
  37. 37.
    Zhanna Sarsenbayeva, Jorge Goncalves, Juan García, Simon Klakegg, Sirkka Rissanen, Hannu Rintamäki, Jari Hannu and Vassilis Kostakos. 2016. Situational impairments to mobile Interaction in cold environments. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 85-96.Google Scholar
  38. 38.
    Valentin Radu, Panagiota Katsikouli, Rik Sarkar and Mahesh K. Marina. 2014. A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, ACM, 280-294.Google Scholar
  39. 39.
    Paul Schlyter. 2010. Radiometry and photometry in astronomy, retrieved 12/08/2016.Google Scholar
  40. 40.
    Sunrise/Sunset Algorithm. http://williams.best.vwh.net/sunrise_sunset_algorithm.htm, retrieved 18/08/2016.
  41. 41.
    Torres-Sospedra J, Montoliu R, Trilles S, Belmonte Ó, Huerta J (2015) Comprehensive analysis of distance and similarity measures for Wi-fi fingerprinting indoor positioning systems. Expert Syst Appl 42(23):9263–9278. doi:10.1016/j.eswa.2015.08.013 CrossRefGoogle Scholar
  42. 42.
    Trepn Power Profiler - Qualcomm Developer Network. https://developer.qualcomm.com/software/trepn-power-profiler, retrieved 27/05/2016.
  43. 43.
    Trepn Profiler – Android. https://play.google.com/store/apps/details?id=com.quicinc.trepn, retrieved 27/05/2016.
  44. 44.
    Khai N. Truong, Thariq Shihipar and Daniel J. Wigdor. 2014. Slide to X: unlocking the potential of smartphone unlocking. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, 3635-3644.Google Scholar
  45. 45.
    Van den Broucke K, Ferreira D, Goncalves J, Kostakos V, De Moor K (2014) Mobile Cloud Storage: A Contextual Experience. In: In International Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, pp 101–110. doi:10.1145/2628363.2628386 Google Scholar
  46. 46.
    Wang F, Huang Z, Yu H, Tian X, Wang X, Huang J (2013) EESM-based fingerprint algorithm for Wi-fi indoor positioning system. In: In International Conference on Communications in China, IEEE, pp 674–679. doi:10.1109/ICCChina.2013.6671197 Google Scholar
  47. 47.
    Yi Wang, Jialiu Lin, Murali Annavaram, Quinn A. Jacobson, Jason Hong, Bhaskar Krishnamachari and Norman Sadeh. 2009. A framework of energy efficient mobile sensing for automatic user state recognition. In Proceedings of the 7th international conference on Mobile systems, applications, and services, 179-192. doi:10.1145/1555816.1555835.
  48. 48.
    Waqar W, Chen Y, Vardy A (2016) Smartphone positioning in sparse Wi-fi environments. Comput Commun 73:108–117. doi:10.1016/j.comcom.2015.09.002 CrossRefGoogle Scholar
  49. 49.
    Xu W, Chen R, Chu T, Kuang L et al (2014) A context detection approach using GPS module and emerging sensors in smartphone platform. In Ubiquitous Positioning Indoor Navigation and Location Based Service, IEEE, pp 156–163Google Scholar
  50. 50.
    Zheng Y, Wu C, Liu Y (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: In Proceedings of the 18th International Conference on Mobile Computing and Networking, ACM, pp 269–280. doi:10.1145/2348543.2348578 Google Scholar
  51. 51.
    Dezhong Yao, Chen Yu, Anind A. K. Dey, Christian Koehler, Geyong Min, Laurence L. T. Yang and Hai Jin. 2014. Energy efficient indoor tracking on smartphones. Futur Gener Comput Syst 39, 44–54. doi:10.1016/j.future.2013.12.032.
  52. 52.
    Zengbin Zhang, Xia Zhou, Weile Zhang, Yuanyang Zhang, Gang Wang, Ben B. Y. Zhao and Haitao Zheng. 2011. I am the antenna: accurate outdoor AP location using smartphones. In Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, ACM, 109–120. doi:10.1145/2030613.2030626.

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Theodoros Anagnostopoulos
    • 1
  • Juan Camilo Garcia
    • 2
  • Jorge Goncalves
    • 2
    • 3
  • Denzil Ferreira
    • 2
  • Simo Hosio
    • 2
  • Vassilis Kostakos
    • 2
    • 3
  1. 1.Ordnance SurveySouthamptonUK
  2. 2.Center for Ubiquitous Computing University of OuluOuluFinland
  3. 3.School of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

Personalised recommendations