Using Active Learning to Allow Activity Recognition on a Large Scale

  • Hande Alemdar
  • Tim L. M. van Kasteren
  • Cem Ersoy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7040)

Abstract

Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine learning technique that allows the algorithm to choose the most informative data points to be annotated. Because the algorithm selects the most informative data points, the amount of the labeled data needed for training the model is reduced. In this study, we propose using active learning methods for activity recognition. We use three different informativeness measures for selecting the most informative data points and evaluate their performances using three real world data sets recorded in a home setting. We show through experiments that the required number of data points is reduced by 80% in House A, 73% in House B, and 66% in House C with active learning.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altun, K., Barshan, B., Tunc, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition 43, 3605–3620 (2010)CrossRefMATHGoogle Scholar
  2. 2.
    Anderson, B., Siddiqi, S., Moore, A.: Sequence selection for active learning (2006)Google Scholar
  3. 3.
    Atallah, L., Lo, B., Ali, R., King, R., Yang, G.Z.: Real-time activity classification using ambient and wearable sensors. IEEE Transactions on Information Technology in Biomedicine 13(6), 1031–1039 (2009)CrossRefGoogle Scholar
  4. 4.
    Biswas, J., Tolstikov, A., Jayachandran, M., Foo, V., Aung, A., Wai, P., Phua, C., Huang, W., Shue, L.: Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia. Annals of Telecommunications 65, 505–521 (2010)CrossRefGoogle Scholar
  5. 5.
    Buettner, M., Prasad, R., Philipose, M., Wetherall, D.: Recognizing daily activities with RFID-based sensors. In: 11th International Conference on Ubiquitous Computing, pp. 51–60. ACM (2009)Google Scholar
  6. 6.
    Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for Activities of Daily Living recognition. The Journal of Supercomputing 54, 29–42 (2010)CrossRefGoogle Scholar
  7. 7.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–298 (2009)CrossRefGoogle Scholar
  8. 8.
    Fletcher, R.R., Dobson, K., Goodwin, M.S., Eydgahi, H., Wilder-Smith, O., Fernholz, D., Kuboyama, Y., Hedman, E.B., Poh, M.Z., Picard, R.W.: iCalm: wearable sensor and network architecture for wirelessly communicating and logging autonomic activity. IEEE Transactions on Information Technology in Biomedicine 14(2), 215–223 (2010)CrossRefGoogle Scholar
  9. 9.
    He, J., Li, H., Tan, J.: Real-time daily activity classification with wireless sensor networks using Hidden Markov Model. In: International Conference of the IEEE Engineering in Medicine and Biology Society 2007, pp. 3192–3195 (2007)Google Scholar
  10. 10.
    Ho, Y., Lu, C., Chen, I., Huang, S., Wang, C., Fu, L.: Active-learning assisted self-reconfigurable activity recognition in a dynamic environment. In: IEEE International Conference on Robotics and Automation, pp. 813–818 (2009)Google Scholar
  11. 11.
    Hong, Y.J., Kim, I.J., Ahn, S.C., Kim, H.G.: Mobile health monitoring system based on activity recognition using accelerometer. Simulation Modelling Practice and Theory 18(4), 446–455 (2010)CrossRefGoogle Scholar
  12. 12.
    Ince, N.F., Min, C.H., Tewfik, A., Vanderpool, D.: Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors. EURASIP Journal on Advances in Signal Processing 2008, 1–12 (2008)CrossRefMATHGoogle Scholar
  13. 13.
    Kao, T.P., Lin, C.W., Wang, J.S.: Development of a portable activity detector for daily activity recognition. In: IEEE International Symposium on Industrial Electronics, pp. 115–120 (2009)Google Scholar
  14. 14.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity Recognition using Cell Phone Accelerometers. In: 4th International Workshop on Knowledge Discovery from Sensor Data, pp. 10–18 (2010)Google Scholar
  15. 15.
    Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: 11th International Conference on Machine Learning, pp. 148–156 (1994)Google Scholar
  16. 16.
    Liu, R., Chen, T., Huang, L.: Research on human activity recognition based on active learning. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 285–290 (2010)Google Scholar
  17. 17.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  18. 18.
    Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Conference on Empirical Methods in Natural Language Processing - EMNLP 2008 (2008)Google Scholar
  19. 19.
    Stikic, M., van Laerhoven, K., Schiele, B.: Exploring semi-supervised and active learning for activity recognition. In: 12th IEEE International Symposium on Wearable Computers (ISWC 2008), pp. 81–88 (2008)Google Scholar
  20. 20.
    van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Transferring Knowledge of Activity Recognition across Sensor Networks. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive Computing. LNCS, vol. 6030, pp. 283–300. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    van Kasteren, T.L.M., Noulas, A., Englebienne, G., Kröse, B.J.A.: Accurate activity recognition in a home setting. In: 10th International Conference on Ubiquitous Computing - UbiComp 2008 (2008)Google Scholar
  22. 22.
    Van Rijsbergen, C.J.: Information Retrieval. Butterworth–Heinemann (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hande Alemdar
    • 1
  • Tim L. M. van Kasteren
    • 1
  • Cem Ersoy
    • 1
  1. 1.Department of Computer Engineering, NETLABBoğaziçi UniversityIstanbulTurkey

Personalised recommendations