Observing Human Activity Through Sensing

  • Sidharta Gautama
  • Martin Atzmueller
  • Vassilis Kostakos
  • Dominique Gillis
  • Simo Hosio
Chapter

Abstract

This chapter gives an overview of technological solutions that allow us to observe human activity during travel or in interaction with each other or with the environment. Different categories of solutions are presented, namely observation by scanning, location-enabled devices and tagging. For each category we look into current innovative solutions, both in applied hardware as in how the data is processed. For each category examples are discussed and the possibilities and bottlenecks of each solution are highlighted.

References

  1. Alani, H., Szomszor, M., Cattuto, C., den Broeck, W.V., Correndo, G., Barrat, A.: Live social semantics. In: Proceedings of the International Semantic Web Conference, pp. 698–714. (2009)Google Scholar
  2. Asakura, Y., Hato, E.: Tracking survey for individual travel behaviour using mobile communication instruments. Transp. Res. C 12, 273–291 (2004)CrossRefGoogle Scholar
  3. Asmundsdottir, R., Chen, Y., van Zuylen, H.J.: Dynamic origin-destination matrix estimation using probe vehicle data as a priori information. In: Barcelo, J., Kuwahara, M. (eds.) Traffic Data Collection and Its Standardization, pp. 89–108. Springer, New York (2010)Google Scholar
  4. Atzmueller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing social interactions at conferences. Inf. Technol. 53(3), 101–107 (2011)Google Scholar
  5. Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: observing social and physical activities. In: Proceedings of the 4th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom), 2012Google Scholar
  6. Atzmueller, M., Doerfel, S., Mitzlaff, F., Hotho, A., Stumme, G.: Face-to-face contacts at a conference: dynamics of communities and roles. In: Modeling and Mining Ubiquitous Social Media, vol. 7472. Springer, Heidelberg, 2012Google Scholar
  7. Atzmueller, M., Hilgenberg, K. Towards capturing social interactions with SDCF: an extensible framework for mobile sensing and ubiquitous data collection. In Proceedings of the 4th International Workshop on Modeling Social Media, ACM, 6 (2013)Google Scholar
  8. Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its applications for ubiquitous social computing. New Rev. Hypermedia Multimedia 20(1), 53–77 (2014)ADSCrossRefGoogle Scholar
  9. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefMATHGoogle Scholar
  10. Barrat, A., Cattuto, C., Szomszor, M., den Broeck, W.V., Alani, H.: Social dynamics in conferences: Analyses of data from the live social semantics application. In: Proceedings of the International Semantic Web Conference, LCNS, vol. 6497, pp. 17–33. (2010)Google Scholar
  11. Bredereck, M., Jiang, X., Korner, M., Denzler, J.: Data association for multi-object tracking-by-detection in multi-camera networks. In: Proceedings of the Sixth International of Conference on Distributed Smart Cameras (ICDSC2012), 2012Google Scholar
  12. Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12(3), 920–939 (2011)CrossRefGoogle Scholar
  13. Bullock, D., Haseman, R., Wasson, J., Spitler, R.: Anonymous bluetooth probes for measuring airport security screening passage time: the indianapolis pilot de-ployment. In: Transportation Research Board 89th Annual Meeting, 2010Google Scholar
  14. Calabrese, F., Di Lorenzo, G., Liu, L., Ratti, C.: Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput. 10, 36–44 (2011a)CrossRefGoogle Scholar
  15. Calabrese, F., Ratti, C., Colonna, M., Lovisolo, P., Parata, D.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011b)CrossRefGoogle Scholar
  16. Cattuto, C., den Broeck, W.V., Barrat, A., Colizza, V., Pinton, J.F., Vespignani, A.: Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One 5(7) (2010)Google Scholar
  17. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal Ubiquit. Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  18. Frendberg, M.: Determining transportation mode through cell phone sensor fusion. PhD thesis, Massachusetts Institute of Technology, Cambridge (2011)Google Scholar
  19. Girardin, F., Calabrese, F., Fiore, F.D., et al.: Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput. 7, 36–43 (2008)CrossRefGoogle Scholar
  20. Gong, H., Chen, C., Bialostozky, E., Lawson, C.T.: A GPS/GIS method for travel mode detection in New York city. Comput. Environ. Urban Syst. 36(2), 131–139 (2011)CrossRefGoogle Scholar
  21. Gonzalez, P., Weinstein, J., Barbeau, S., Labrador, M., Winters, P., Georggi, N.L., Perez, R.: Automating mode detection using neural networks and assisted gps data collected using gps-enabled mobile phones. In: 15th World Congress on Intelligent Transportation Systems, 2008Google Scholar
  22. Hato, E.: Development of behavioral context addressable loggers in the shell for travel-activity analysis. Transp. Res. C 18, 55–67 (2010)CrossRefGoogle Scholar
  23. Isella, L., Romano, M., Barrat, A., Cattuto, C., Colizza, V., Van den Broeck, W., Tozzi, A.E.: Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS One 6(2), e17144 (2011)ADSCrossRefGoogle Scholar
  24. Jonsson, F.: Determining transportation mode in mobile phones using human agent movements. USCCS 73 (2010)Google Scholar
  25. Kim, M., Kotz, D.: Modeling users' mobility among WiFi access points, Workshop on Wireless traffic measurements and modeling, pp.~19–24. USENIX Association (2005)Google Scholar
  26. Kostakos, V., O’Neill, E.: Cityware: Urban computing to bridge online and real-world social networks. In: Foth, M. (ed.) Handbook of Research on Urban Infor-matics: The Practice and Promise of the Real-Time City, pp. 195–204. IGI Global, Hershey/London (2008)Google Scholar
  27. Kostakos, V., O’Neill, E., Penn, A., et al.: Brief encounters: Sensing, modeling and visualizing urban mobility and copresence networks. ACM TOCHI 17, 2 (2010)CrossRefGoogle Scholar
  28. Kruegle H.: CCTV Surveillance: Video Practices and Technology. Elsevier Butterworth-Heinemann, Burlington/Oxford (2011)Google Scholar
  29. Macek, B.E., Scholz, C., Atzmueller, M., Stumme, G.: Anatomy of a conference. In: Proceedings of the 23rd ACM conference on Hypertext and Social Media, pp. 245–254. ACM (2012)Google Scholar
  30. Malinovskiy, Y., Wu, Y.J., Wang, Y., Lee, U.K.: Field experiments on bluetooth-based travel time data collection. In: Transportation Research Board 89th Annual Meeting, 2010Google Scholar
  31. Manzoni, V., Maniloff, D., Kloeckl, K., Ratti, C.: Transportation mode identification and real-time CO2 emission estimation using smartphones. Technical report, Massachusetts Institute of Technology, Cambridge (2010)Google Scholar
  32. Morbee, M., Tessens, L., Aghajan, H., Philips, W.: Dempster-Shafer based multi-view occupancy maps. Electron. Lett. 46(5), 341–343 (2010)CrossRefGoogle Scholar
  33. Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West R., Boda, P.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68. ACM, New York (2009)Google Scholar
  34. Nham, B., Siangliulue, K., Yeung, S.: Predicting Mode of Transport from iPhone Accelerometer Data. Technical report, Stanford University (2008)Google Scholar
  35. O’Neill, E., Kostakos, V., Kindberg, T., Penn, A., Fraser, D. S., Jones, T.: Instrumenting the city: Developing methods for observing and understanding the digital cityscape. In: UbiComp 2006: Ubiquitous Computing, pp. 315–332. Springer, Berlin Heidelberg (2006)Google Scholar
  36. Pentland, A.: Looking at people: Sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 107–119 (2000)CrossRefGoogle Scholar
  37. Quercia, D., Di Lorenzo, G., Calabrese, F., Ratti, C.: Mobile phones and outdoor advertising: measurable advertising. IEEE Pervasive Comput. 10, 28–36 (2011)CrossRefGoogle Scholar
  38. Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, 23–27 July 2007Google Scholar
  39. Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sens. Netw. (TOSN) 6(2), 13 (2010)Google Scholar
  40. Reinthaler, M., Zajicek, J.: Real time route analysis based on floating car technology. In: 18th IASTED International Conference Modelling and Simulation, pp. 609–612. Montreal, Canada (2007)Google Scholar
  41. Scholz, C., Atzmueller, M., Stumme, G.: On the predictability of human contacts: influence factors and the strength of stronger ties. In: Proceedings of the Fourth ASE/IEEE International Conference on Social Computing (SocialCom), IEEE Computer Society, Boston, MA, 2012Google Scholar
  42. Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., Stumme, G.: New insights and methods for predicting face-to-face contacts. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM’13), 2013Google Scholar
  43. Stehle, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.F., Quaggiotto, M., den Broeck, W.V., Regis, C., Lina, B., Vanhems, P.: High-resolution measurements of face-to-face contact patterns in a primary school. CoRR (2011). abs/1109.1015Google Scholar
  44. Taale, H., Hoog, A.D., Smulders, S., Tool, O.: The results of a Dutch experiment with floating car data. In: Control in Transportation Systems 2000: A Proceedings of the Volume from the 9th IFAC Symposium, vol. 1. Braunschweig, Germany, 2001Google Scholar
  45. Torp, K., Lahrmann, H.S.: Floating car data for traffic monitoring. In: ITS at the Crossroads of European Transport, ERTICO—ITS Europe, 2005Google Scholar
  46. Tsui, A., Shalaby, A.: Enhanced system for link and mode identification for personal travel surveys based on global positioning systems. Transp. Res. Rec. J. Transp. Res. Board 1972(1), 38–45 (2006)CrossRefGoogle Scholar
  47. Versichele, M., Neutens, T., Delafontaine, M., Van de Weghe, N.: The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities. Appl. Geogr. 32(2), 208–220 (2012)CrossRefGoogle Scholar
  48. Vlassenroot, S., Gillis, D., Bellens, R., Gautama, S.: The use of smartphone applications in the collection of travel behavior data. In: 9th ITS European Congress, Proceedings, ERTICO, 2013Google Scholar
  49. Weinzerl, J., Hagemann, W.: Automatische Erfassung von Umsteigern per Bluetooth Technologie. Nahverkehrspraxis 3, 18–19 (2007)Google Scholar
  50. Xie, X., Grünwedel, S., Jelaca, V., Niño Castañeda, J., Van Haerenborgh, D., Van Cauwelaert, D., Van Hese, P., et al.: Learning about objects in the meeting rooms from people trajectories. In: 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC), 2012Google Scholar
  51. Xu, B., Chin, A., Wang, H., Chang, L., Zhang, K., Yin, F., Wang, H., Zhang, L.: Physical proximity and online user behavior in an indoor mobile social networking application. In: Proceedings of the 4th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom2011), 2011Google Scholar
  52. Yang, Y., Toida, T., Hong, C.: Transportation prediction using build-in triaxial accelerometer in cell phone. In: International Conference on Business Information, Bai, 2010Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Sidharta Gautama
    • 1
  • Martin Atzmueller
    • 2
  • Vassilis Kostakos
    • 3
  • Dominique Gillis
    • 1
  • Simo Hosio
    • 3
  1. 1.Ghent University, i-KNOWGentBelgium
  2. 2.University of Kassel, Research Center for Information System DesignKasselGermany
  3. 3.University of Oulu, Center for Ubiquitous ComputingOuluFinland

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