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Using Graphs to Improve Activity Prediction in Smart Environments Based on Motion Sensor Data

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Toward Useful Services for Elderly and People with Disabilities (ICOST 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6719))

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Abstract

Activity Recognition in Smart Environments presents a difficult learning problem. The focus of this paper is a 10-class activity recognition problem using motion sensor events over time involving multiple residents and non-scripted activities. This paper presents the results of using three different graph-based approaches to this problem, and compares them to a non-graph SVM approach. The graph-based approaches are generating feature vectors using frequent subgraphs for classification by an SVM, an SVM using a graph kernel and nearest neighbor approach using a graph comparison measure. None demonstrate significantly superior accuracy compared to the non-graph SVM, but all demonstrate strongly uncorrelated error both against the base SVM and each other. An ensemble is created using the non-graph SVM, Frequent Subgraph SVM, Graph Kernel SVM, and Nearest Neighbor. Error is shown to be highly uncorrelated between these four. This ensemble substantially outperforms all of the approaches alone. Results are shown for a 10-class problem arising from smart environments, and a 2-class one-vs-all version of the same problem.

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References

  1. Tracking activities in complex settings using smart environment technologies. International Journal of BioSciences, Psychiatry and Technology 1, 25–36 (2009)

    Google Scholar 

  2. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software available at (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

  3. Cook, D.J., Holder, L.B.: Graph-based data mining. IEEE Intelligent Systems 15(2), 32–41 (2000)

    Article  Google Scholar 

  4. Cook, D.J., Holder, L.B.: Mining Graph Data. John Wiley & Sons, Chichester (2006)

    Book  MATH  Google Scholar 

  5. Crandall, A., Cook, D.: Attributing events to individuals in multi-inhabitant environments. In: Proceedings of the International Conference on Intelligent Environments (2008)

    Google Scholar 

  6. Crandall, A., Cook, D.: Coping with multiple residents in a smart environment. Journal of Ambient Intelligence and Smart Environments 1(4), 323–334 (2009)

    Google Scholar 

  7. Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering 17(8), 1036–1050 (2005)

    Article  Google Scholar 

  8. Gaertner, T., Flach, P., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 129–143. Springer, Heidelberg (2003), http://www.cs.bris.ac.uk/Publications/Papers/2000555.pdf

    Chapter  Google Scholar 

  9. Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9, 48–53 (2010)

    Article  Google Scholar 

  10. Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)

    Article  Google Scholar 

  11. Neuhaus, M., Bunke, H.: Edit distance-based kernel functions for structural pattern classification. Pattern Recognition 39(10), 1852–1863 (2006)

    Article  MATH  Google Scholar 

  12. Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: KDD 2004: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 647–652. ACM, New York (2004)

    Chapter  Google Scholar 

  13. Scholkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45(11), 2758–2765 (1997)

    Article  Google Scholar 

  14. Vishwanathan, S., Borgwardt, K., Schraudolph, N.: Fast computation of graph kernels. Advances in Neural Information Processing Systems 19, 1449 (2007)

    Google Scholar 

  15. Wang, W., Xu, Z., Lu, W., Zhang, X.: Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing 55(3), 643–664 (2003)

    Article  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Long, S.S., Holder, L.B. (2011). Using Graphs to Improve Activity Prediction in Smart Environments Based on Motion Sensor Data. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds) Toward Useful Services for Elderly and People with Disabilities. ICOST 2011. Lecture Notes in Computer Science, vol 6719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21535-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-21535-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21534-6

  • Online ISBN: 978-3-642-21535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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