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Mobile Activity Recognition Using Ubiquitous Data Stream Mining

  • João Bártolo Gomes
  • Shonali Krishnaswamy
  • Mohamed M. Gaber
  • Pedro A. C. Sousa
  • Ernestina Menasalvas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7448)

Abstract

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the rich sensory data that is available on today’s smart phones and other wearable sensors. The state of the art in mobile activity recognition research has focused on traditional classification learning techniques. In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental manner. The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site; iii) adaptation of the model as the user’s activity profile changes. In our extensive experimental results using a recent benchmarking activity recognition dataset, we show that MARS can achieve similar accuracy when compared with traditional classifiers for activity recognition, while at the same time being scalable and efficient in terms of the mobile device resources consumption. MARS has been implemented on the Android platform for empirical evaluation.

Keywords

Mobile Device Activity Recognition Wearable Sensor Android Platform Locomotion Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Gomes, J.B., Krishnaswamy, S., Gaber, M., Sousa, P.A.C., Menasalvas, E.: Mars: a personalised mobile activity recognition system. In: Proceedings of the International Conference on Mobile Data Management, MDM 2012, Bengaluru, India, July 23-26, IEEE (2012)Google Scholar
  3. 3.
    Bartolo Gomes, J., Menasalvas, E., Sousa, P.A.C.: Situation-aware data stream mining service for ubiquitous applications. In: 2010 Eleventh International Conference on Mobile Data Management (MDM), pp. 360–365. IEEE (2010)Google Scholar
  4. 4.
    Gama, J., Sebastiao, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338. ACM, New York (2009)CrossRefGoogle Scholar
  5. 5.
    Krishnaswamy, S., Gama, J., Gaber, M.M.: Advances in data stream mining for mobile and ubiquitous environments. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2607–2608. ACM (2011)Google Scholar
  6. 6.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2011)CrossRefGoogle Scholar
  7. 7.
    Preece, S., Goulermas, J., Kenney, L., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors – a review of classification techniques. Physiological Measurement 30(4), R1–R33 (2009)CrossRefGoogle Scholar
  8. 8.
    Sagha, H., Digumarti, S.T., Millán, J.d.R., Lozano, R.C., Calatroni, A., Roggen, D., Tröster, G.: Benchmarking classification techniques using the Opportunity human activity dataset. IEEE International Conference on Systems, Man, and Cybernetics (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • João Bártolo Gomes
    • 1
  • Shonali Krishnaswamy
    • 1
  • Mohamed M. Gaber
    • 2
  • Pedro A. C. Sousa
    • 3
  • Ernestina Menasalvas
    • 4
  1. 1.Institute for Infocomm Research (I2R), A*STARSingapore
  2. 2.School of ComputingUniversity of PortsmouthUnited Kingdom
  3. 3.Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaPortugal
  4. 4.Facultad de InformaticaUniversidad PolitecnicaMadridSpain

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