Advertisement

Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis

  • Dorothy Monekosso
  • Paolo Remagnino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)

Abstract

This paper describes a data generator that produces synthetic data to simulate observations from an array of environment monitoring sensors. The overall goal of our work is to monitor the well-being of one occupant in a home. Sensors are embedded in a smart home to unobtrusively record environmental parameters. Based on the sensor observations, behavior analysis and modeling are performed. However behavior analysis and modeling require large data sets to be collected over long periods of time to achieve the level of accuracy expected. A data generator - was developed based on initial data i.e. data collected over periods lasting weeks to facilitate concurrent data collection and development of algorithms. The data generator is based on statistical inference techniques. Variation is introduced into the data using perturbation models.

Keywords

Synthetic data generation perturbation model statistical analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brumitt, B., Meyers, B., Krumm, J., Hale, M., Harris, S., Shafer, S.: EasyLiving: Technologies for Intelligent Environments. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 12–29. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    The iDorm project home page, Essex University, UK, http://iieg.essex.ac.uk/idorm.htm
  3. 3.
    The iRoom project home page, Stanford, http://iwork.stanford.edu/
  4. 4.
    The HyperMedia studio project home page, UCLA, http://hypermedia.ucla.edu/
  5. 5.
    The MavHome project home page, University of Texas, Arlington (2005), http://cygnus.uta.edu/mavhome/
  6. 6.
    Tapia, E., Munguia, S., Intille, S., Larson, K.: Activity recognition in the home setting using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Mühlenbrock, M., Brdiczka, O., Snowdon, D., Meunier, J.-L.: Learning to detect user activity and availability from a variety of sensor data. In: Proceedings of the Second IEEE Conference on Pervasive Computing and Communications, Orlando, FL (2007)Google Scholar
  8. 8.
    Brdiczka, O., Vaufreydaz, D., Maisonnasse, J., Reignier, P.: Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection. In: 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), Athens, Greece, June 7-9, pp. 195–203 (2006)Google Scholar
  9. 9.
    Brdiczka, O., et al.: Detecting Individual Activities from Video in a Smart Home. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 363–370. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Rivera-Illingworth, F., Callaghan, V., Hagras, H.A.: Neural Network Agent Based Approach to Activity Detection, in AmI Environments. In: IEE International Workshop, Intelligent Environments (IE 2005), Colchester, UK, June 28-29 (2005)Google Scholar
  11. 11.
    Doctor, F., Hagras, H.A., Callaghan, V.: An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 35(1), 55–65Google Scholar
  12. 12.
    Mozer, M.C.: Lessons from an adaptive house. In: Cook, D., Das, R. (eds.) Smart environments: Technologies, protocols, and applications, pp. 273–294. J. Wiley & Sons, HobokenGoogle Scholar
  13. 13.
    Rao, S., Cook, D.J.: Predicting Inhabitant Actions Using Action and Task Models with Application to Smart Homes. International Journal of Artificial Intelligence Tools 13(1), 81–100 (2004)CrossRefGoogle Scholar
  14. 14.
    Hori, T., Nishida, Y., And Murakami, S.: A Pervasive Sensor System for Nursing Care Support. In: Ambient Intelligence Techniques and Applications, Computer Science. Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Cesta, A., et al.: Robotic, Sensory and Problem-Solving Ingredients for the Future Home. In: Ambient Intelligence Techniques and Applications, Computer Science. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Baird, H.: State of the art of document image degradation modeling. In: Proc. 4th IAPR Workshop on Document Analysis Systems, pp. 1–16 (2000)Google Scholar
  17. 17.
    Lu, X., Jain, A.K.: Ber. resampling for face recognition chem. In: 4th Internat. Conf. on Audio and Video based Biometric Person Authentication, pp. 869–877 (2003)Google Scholar
  18. 18.
    Chen, J., Chen, X.L., Gao, W.: Resampling for face detection by self-adaptive genetic algorithm. In: Proc. Internat. Conf. on Pattern Recognition, pp. 822–825 (2004)Google Scholar
  19. 19.
    Jiang, F., Gao, W., Yao, H., Zhao, D., Chen, X.: Synthetic data generation technique in Signer-independent sign language recognition. Pattern Recogn. Lett. 30(5) (2009)Google Scholar
  20. 20.
    Varga, T., Bunke, H.: Effects of Training Set Expansion in Handwriting Recognition Using Synthetic Data, pp. 200–203 (2003)Google Scholar
  21. 21.
    Varga, T., Bunke, H.: Perturbation models for generating synthetic training data in handwriting recognition. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition, pp. 333–360. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dorothy Monekosso
    • 1
  • Paolo Remagnino
    • 2
  1. 1.CSRIUniversity of UlsterJordanstownUK
  2. 2.CISMKingston UniversityLondonUK

Personalised recommendations