Abstract
One important application of Body Sensor Networks is action recognition. Action recognition often implicitly requires partitioning the sensor data into intervals, then labeling the partitions according to the actions each represents or as a non-action. The temporal partitioning stage is called segmentation and the labeling is called classification. While many effective methods exist for classification, segmentation remains problematic. We present a technique inspired by continuous speech recognition that combines segmentation and classification using Hidden Markov Models. This technique is distributed and only involves limited data sharing between sensor nodes. We show the results of this technique and the bandwidth savings over full data transmission.
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References
Hausdorff, J., Cudkowicz, M., Firtion, R., Wei, J., Goldberger, A.: Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov. Disord. 13(3), 428–437 (1998)
Aminian, K., Najafi, B., Büla, C., Leyvraz, P., Robert, P.: Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of Biomechanics 35(5), 689–699 (2002)
Nait-Charif, H., McKenna, S.: Activity summarisation and fall detection in a supportive home environment. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 4 (2004)
Castelli, G., Rosi, A., Mamei, M., Zambonelli, F.: A Simple Model and Infrastructure for Context-Aware Browsing of the World. In: IEEE International Conference on Pervasive Computing and Communications, pp. 229–238 (2007)
Ward, J., Lukowicz, P., Tröster, G., Starner, T.: Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1553–1567 (2006)
Ghasemzadeh, H., Guenterberg, E., Gilani, K., Jafari, R.: Action coverage formulation for power optimization in body sensor networks. In: Design Automation Conference, ASPDAC 2008, Asia and South Pacific, pp. 446–451 (2008)
Lv, F., Nevatia, R.: Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, p. 359. Springer, Heidelberg (2006)
Bao, L., Intille, 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)
Bao, L.: Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions. PhD thesis, Massachusetts Institute of Technology (2003)
Van Laerhoven, K., Gellersen, H.: Spine versus Porcupine: a Study in Distributed Wearable Activity Recognition. In: Proc. of the Eighth IEEE Intl. Symposium on Wearable Computers, vol. 1, pp. 142–149
Courses, E., Surveys, T., View, T.: Analysis of low resolution accelerometer data for continuous human activity recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2008, pp. 3337–3340 (2008)
Lester, J., Choudhury, T., Kern, N., Borriello, G., Hannaford, B.: A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2005) (2005)
Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks 38(4), 393–422 (2002)
Polastre, J., Szewczyk, R., Culler, D.: Telos: enabling ultra-low power wireless research. In: Proceedings of the 4th international symposium on Information processing in sensor networks. IEEE Press, Piscataway (2005)
Rabiner, L., Juang, B.: An introduction to hidden Markov models. ASSP Magazine, IEEE 3(1 Part 1), 4–16 (1986)
Jurafsky, D., Martin, J., Kehler, A., Vander Linden, K., Ward, N.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press, Cambridge (2000)
Lee, H., Kim, J.: An HMM-Based Threshold Model Approach for Gesture Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 961–973 (1999)
Johnson, S.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
Hartigan, J., Wong, M.: A K-means clustering algorithm. JR Stat. Soc. Ser. C-Appl. Stat. 28, 100–108 (1979)
Fraley, C., Raftery, A.: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal 41(8), 578–588 (1998)
Figueiredo, M., Jain, A.: Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 381–396 (2002)
Stoica, P., Selen, Y.: Model-order selection: a review of information criterion rules. Signal Processing Magazine, IEEE 21(4), 36–47 (2004)
Biem, A.: A model selection criterion for classification: Application to hmm topology optimization. In: Seventh International Conference on Document Analysis and Recognition, vol. 1, pp. 104–108 (2003)
Yang, A., Jafari, R., Sastry, S., Bajcsy, R.: Distributed Recognition of Human Actions Using Wearable Motion Sensor Networks. Journal of Ambient Intelligence and Smart Environments 1, 1–5 (2009)
Yoon, H., Lee, J., Yang, H.: An online signature verification system using hidden Markov model in polar space. In: Proceedings of Eighth International Workshop on Frontiers in Handwriting Recognition, 2002, pp. 329–333 (2002)
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Guenterberg, E., Ghasemzadeh, H., Loseu, V., Jafari, R. (2009). Distributed Continuous Action Recognition Using a Hidden Markov Model in Body Sensor Networks. In: Krishnamachari, B., Suri, S., Heinzelman, W., Mitra, U. (eds) Distributed Computing in Sensor Systems. DCOSS 2009. Lecture Notes in Computer Science, vol 5516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02085-8_11
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DOI: https://doi.org/10.1007/978-3-642-02085-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02084-1
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