Abstract
The automatic recognition of human activities such as cooking, showering and sleeping allows many potential applications in the area of ambient intelligence. In this paper we show that using a hierarchical structure to model the activities from sensor data can be very beneficial for the recognition performance of the model. We present a two-layer hierarchical model in which activities consist of a sequence of actions. During training, sensor data is automatically clustered into clusters of actions that best fit to the data, so that sensor data only has to be labeled with activities, not actions. Our proposed model is evaluated on three real world datasets and compared to two non-hierarchical temporal probabilistic models. The hierarchical model outperforms the non-hierarchical models in all datasets and does so significantly in two of the three datasets.
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References
Bilmes, J.: A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report TR-97-021, International Computer Science Institute (1997)
Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, CVPR, pp. 838–845. IEEE Computer Society, Washington, DC, USA (2005)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Activity Recognition in Pervasive Intelligent Environments. In: Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software, ch. 8, pp. 165–186. Atlantis Press (2011)
van Kasteren, T.L.M., Noulas, A., Englebienne, G., Kröse, B.J.A.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, Ubicomp, pp. 1–9. ACM, New York (2008)
Lühr, S., Bui, H.H., Venkatesh, S., West, G.A.W.: Recognition of human activity through hierarchical stochastic learning. In: Proceedings of the 1st International Conference on Pervasive Computing and Communications, PerCom, pp. 416–422. IEEE Computer Society, Washington, DC, USA (2003)
Muncaster, J., Ma, Y.: Hierarchical model-based activity recognition with automatic low-level state discovery. Journal of Multimedia 2(5), 66 (2007)
Murphy, K., Paskin, M.: Linear time inference in hierarchical hmms. In: Advances in Neural Information Processing Systems 14, NIPS (2001)
Nguyen, N.T., Phung, D.Q., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden markov models. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, CVPR, pp. 955–960. IEEE Computer Society, Washington, DC, USA (2005)
Patterson, D.J., Fox, D., Kautz, H.A., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Proceedings of the 9th International Symposium on Wearable Computers, ISWC, pp. 44–51. IEEE Computer Society (2005)
Turaga, P., Chellappa, R., Subrahmanian, V., Udrea, O.: Machine recognition of human activities: A survey. IEEE Trans. on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)
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© 2011 Springer-Verlag Berlin Heidelberg
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van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A. (2011). Hierarchical Activity Recognition Using Automatically Clustered Actions. In: Keyson, D.V., et al. Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25167-2_9
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DOI: https://doi.org/10.1007/978-3-642-25167-2_9
Publisher Name: Springer, Berlin, Heidelberg
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