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Predictability Analysis of Aperiodic and Periodic Model for Long-Term Human Mobility Using Ambient Sensors

  • Danaipat Sodkomkham
  • Roberto Legaspi
  • Ken-ichi Fukui
  • Koichi Moriyama
  • Satoshi Kurihara
  • Masayuki Numao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8940)

Abstract

The predictive technique proposed in this project was initially designed for an indoor smart environment wherein intrusive tracking techniques, such as cameras, mobile phones, and GPS tracking systems, could not be appropriately utilized. Instead, we installed simple motion detection sensors in various areas of the experimental space and observed movements. However, the data collected cannot provide as much information about human mobility as data from a GPS or mobile phone. In this paper, we conducted an exhaustive analysis to determine the predictability of future mobility of people using only this limited dataset. Furthermore, we proposed an aperiodic and periodic predictive technique for long-term human mobility prediction that works well with our limited dataset. The evaluation of the dataset collected of the movement and daily activity in the smart space for three months shows that our model is able to predict future mobility and activities of participants in the smart environment setting with high accuracy – even for a month in advance.

Keywords

Human mobility Smart environment Long-term prediction Fano’s inequality Predictability analysis 

Notes

Acknowledgement

This work was partly supported by JSPS Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation and JSPS Core-to-Core Program, A. Advanced Research Networks.

References

  1. 1.
    Das, B., Chen, C., Dasgupta, N., Cook, D.J., Seelye, A.M.: Automated prompting in a smart home environment. In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops (ICDMW ’10), pp. 1045–1052 (2010)Google Scholar
  2. 2.
    Das, B., Cook, D.J., Schmitter-Edgecombe, M., Seelye, A.M.: PUCK: an automated prompting system for smart environments: toward achieving automAted Prompting-challenges Involved. Pers. Ubiquitous Comput. 16(7), 859–873 (2012)CrossRefGoogle Scholar
  3. 3.
    Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’09), pp. 637–646 (2009)Google Scholar
  4. 4.
    Krumm, J., Horvitz, E.: Predestination: inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Song, L., Deshpande, U., Kozat, U., Kotz, D., Jain, R.: Predictability of WLAN mobility and its effects on bandwidth provisioning. In: Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM ’06), pp. 1–13 (2006)Google Scholar
  6. 6.
    Haddadi, H., Hui, P., Brown, I.: MobiAd: private and scalable mobile advertising. In: Proceedings of the Fifth ACM International Workshop on Mobility in the Evolving Internet Architecture (MobiArch ’10), pp. 33–38 (2010)Google Scholar
  7. 7.
    Yu, S.I., Yang, Y., Hauptmann, A.: Harry Potter’s Marauder’s Map: localizing and tracking multiple persons-of-interest by nonnegative discretization. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’13), pp. 3714–3720 (2013)Google Scholar
  8. 8.
    Roth, S.: Discrete-continuous optimization for multi-target tracking. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’12), pp. 1926–1933 (2012)Google Scholar
  9. 9.
    Beleznai, C., Schreiber, D., Rauter, M.: Pedestrian detection using GPU-accelerated multiple cue computation. In: Computer Vision and Pattern Recognition Workshops (CVPRW ’11), pp. 58–65 (2011)Google Scholar
  10. 10.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  11. 11.
    Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T.: NextPlace: a spatio-temporal prediction framework for pervasive systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 152–169. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Baeg, M., Park, J.H., Koh, J., Park, K.W., Baeg, M.H.: Building a smart home environment for service robots based on RFID and sensor networks. In: International Conference on Control, Automation and Systems (ICCAS ’07), pp. 1078–1082 (2007)Google Scholar
  14. 14.
    Hussain, S., Schaffner, S., Moseychuck, D.: Applications of wireless sensor networks and RFID in a smart home environment. In: Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference (CNSR ’09), pp. 153–157 (2009)Google Scholar
  15. 15.
    Pei, J., Pinto, H., Chen, Q., Han, J., Mortazavi-Asl, B., Dayal, U., Hsu, M.C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering (ICDE ’01), pp. 215–224 (2001)Google Scholar
  16. 16.
    Fano, R.M.: Transmission of information: a statistical theory of communications. Am. J. Phys. 29, 793–794 (1961)CrossRefGoogle Scholar
  17. 17.
    Navet, N., Chen, S.H.: On predictability and profitability: Would gp induced trading rules be sensitive to the observed entropy of time series? In: Brabazon, A., O’Neill, M. (eds.) Natural Computing in Computational Finance. SCI, vol. 100, pp. 197–210. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Sadilek, A., Krumm, J.: Far out: predicting long-term human mobility. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI ’12) (2012)Google Scholar
  19. 19.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)CrossRefMathSciNetGoogle Scholar
  20. 20.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  21. 21.
    Ferris, B., Fox, D., Lawrence, N.D.: Wifi-slam using gaussian process latent variable models. In: IJCAI, vol. 7, pp. 2480–2485 (2007)Google Scholar
  22. 22.
    Ferris, B., Haehnel, D., Fox, D.: Gaussian processes for signal strength-based location estimation. In: Proceedings of Robotics Science and Systems, Citeseer (2006)Google Scholar
  23. 23.
    Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th annual international conference on Mobile computing & networking, pp. 27–38, ACM (2013)Google Scholar
  24. 24.
    Cielniak, G., Bennewitz, M., Burgard, W.: Where is...? learning and utilizing motion patterns of persons with mobile robots. In: IJCAI, pp. 909–914 (2003)Google Scholar
  25. 25.
    Nguyen, N., Venkatesh, S., Bui, H.: Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association. In: BMVC 2006: Proceedings of the 17th British Machine Vision Conference, British Machine Vision Association, pp. 1239–1248 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Danaipat Sodkomkham
    • 1
  • Roberto Legaspi
    • 2
  • Ken-ichi Fukui
    • 1
  • Koichi Moriyama
    • 1
  • Satoshi Kurihara
    • 1
  • Masayuki Numao
    • 1
  1. 1.Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan
  2. 2.Research Organization of Information and SystemsTransdisciplinary Research Integration Center, The Institute of Statistical MathematicsTokyoJapan

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