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Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model

Published:01 August 2012Publication History
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Abstract

One important application of body sensor networks is action recognition. Action recognition often implicitly requires partitioning sensor data into intervals, then labeling the partitions according to the action that 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 across several sensor nodes. We show the results of this technique and the bandwidth savings over full data transmission.

References

  1. Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. 2002. Wireless sensor networks: a survey. Comput. Netw. 38, 4, 393--422. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Aminian, K., Najafi, B., Büla, C., Leyvraz, P., and Robert, P. 2002. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J. Biomech. 35, 5, 689--699.Google ScholarGoogle ScholarCross RefCross Ref
  3. Bao, L. 2003. Physical activity recognition from acceleration data under semi-naturalistic conditions. Ph.D. dissutation, Massachusetts Institute of Technology, Cambridge, MA.Google ScholarGoogle Scholar
  4. Bao, L. and Intille, S. 2004. Activity recognition from user-annotated acceleration data. In Proceedings of the 2nd International Conference on Lecture Notes in Computer Science Pervasive Computing. vol. 3001, Springer, Berlin, 1--17.Google ScholarGoogle Scholar
  5. Biem, A. 2003. A model selection criterion for classification: Application to hmm topology optimization. In Proceedings of the 7th International Conference on Document Analysis and Recognition. 104--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Castelli, G., Rosi, A., Mamei, M., and Zambonelli, F. 2007. A simple model and infrastructure for context-aware browsing of the world. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications. 229--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., Hightower, J., Klasnja, P., Koscher, K., LaMarca, A., Landay, J. A., and Lester, J. 2008. The mobile sensing platform: An embedded system for capturing and recognizing human activities. IEEE Pervasive Mag. Special Issue on Activity-Based Computing.Google ScholarGoogle Scholar
  8. Courses, E., Surveys, T., and View, T. 2008. Analysis of low resolution accelerometer data for continuous human activity recognition. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’08). 3337--3340.Google ScholarGoogle Scholar
  9. Figueiredo, M. and Jain, A. 2002. Unsupervised learning of finite mixture models. IEEE Tran. Pattern Anal. Mach. Intell. 24, 3, 381--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fraley, C. and Raftery, A. 1998. How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J. 41, 8, 578--588.Google ScholarGoogle ScholarCross RefCross Ref
  11. Ghasemzadeh, H., Guenterberg, E., Gilani, K., and Jafari, R. 2008. Action coverage formulation for power optimization in body sensor networks. In Proceedings of the Asia and South Pacific Design Automation Conference (ASPDAC’08). 446--451. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Guenterberg, E., Ostadabbas, S., Ghasemzadeh, H., and Jafari, R. 2009. An automatic segmentation technique in body sensor networks based on signal energy. In Proceedings of the 4th International Conference of Body Area Networks (BodyNets’09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Guenterberg, E., Yang, A. Y., Ghasemzadeh, H., Jafari, R., Bajcsy, R., and Sastry, S. S. 2009. A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors. IEEE Trans. Inform. Technol. Biomed. 13, 6, 1019--10300. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hartigan, J. and Wong, M. 1979. A K-means clustering algorithm. JR Stat. Soc. Ser. C-Appl. Stat. 28, 100--108.Google ScholarGoogle ScholarCross RefCross Ref
  15. Hausdorff, J., Cudkowicz, M., Firtion, R., Wei, J., and Goldberger, A. 1998. Gait variability and basal ganglia disorders: Stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mo. Disord. 13, 3, 428--37.Google ScholarGoogle ScholarCross RefCross Ref
  16. Johnson, S. 1967. Hierarchical clustering schemes. Psychometrika 32, 3, 241--254.Google ScholarGoogle ScholarCross RefCross Ref
  17. Jurafsky, D., Martin, J., Kehler, A., Vander Linden, K., and Ward, N. 2000. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kashi, R., Hu, J., Nelson, W., and Turin, W. 1998. A hidden Markov model approach to online handwritten signature verification. Int. J. Doc. Anal. Recog. 1, 2, 102--109.Google ScholarGoogle ScholarCross RefCross Ref
  19. Lee, H. and Kim, J. 1999. An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21, 10, 961--973. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lester, J., Choudhury, T., Kern, N., Borriello, G., and Hannaford, B. 2005. A hybrid discriminative/generative approach for modeling human activities. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’05). Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Lv, F. and Nevatia, R. 2006. Recognition and segmentation of 3-D human action using HMM and multi-class AdaBoost. In Proceedings of the 9th European Conference on Computer Vision (ECCV’06). Lecture Notes in Computer Science vol. 3954, Springer, Berlin, 359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Nait-Charif, H. and McKenna, S. 2004. Activity summarisation and fall detection in a supportive home environment. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Ouchi, K., Suzuki, T., and Doi, M. 2004. LifeMinder: A wearable healthcare support system with timely instruction based on the user’s context. In Proceedings of the 8th IEEE International Workshop on Advanced Motion Control (AMC’04). 445--450.Google ScholarGoogle Scholar
  24. Polastre, J., Szewczyk, R., and Culler, D. 2005. Telos: Enabling ultra-low power wireless research. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Quwaider, M. and Biswas, S. 2008. Body posture identification using hidden Markov model with a wearable sensor network. In Proceedings of the ICST 3rd International Conference on Body Area Networks. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Rabiner, L. and Juang, B. 1986. An introduction to hidden Markov models. ASSP Mag. 3, 1, 4--16. See also IEEE Signal Process. Mag.Google ScholarGoogle ScholarCross RefCross Ref
  27. Raudys, S. and Jain, A. 1991. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13, 3, 252--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Reynolds, D. and Rose, R. 1995. Robust text-independent speaker identification using Gaussianmixture speaker models. IEEE Trans. Speech Audio Process. 3, 1, 72--83.Google ScholarGoogle ScholarCross RefCross Ref
  29. Rosenberg, A., Siohan, O., and Parathasarathy, S. 1998. Speaker verification using minimum verification error training. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.Google ScholarGoogle Scholar
  30. Sheridan, P., Solomont, J., Kowall, N., and Hausdorff, J. 2003. Influence of executive function on locomotor function: Divided attention increases gait variability in Alzheimer’s disease. J. Am. Geriatrics Soc. 51, 11, 1633--1637.Google ScholarGoogle ScholarCross RefCross Ref
  31. Stoica, P. and Selen, Y. 2004. Model-order selection: A review of information criterion rules. IEEE Signal Process. Mag. 21, 4, 36--47.Google ScholarGoogle ScholarCross RefCross Ref
  32. Van Laerhoven, K. and Gellersen, H. 2004. Spine versus Porcupine: A study in distributed wearable activity recognition. In Proceedings of the 8th IEEE International Symposium on Wearable Computers. 142--149. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Ward, J., Lukowicz, P., Tröster, G., and Starner, T. 2006. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Trans. Pattern Anal. Mach. Intell. 28, 10, 1553--1567. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Wessel, F., Macherey, K., Schluter, R., fur Inf, L., and Aachen, T. 1998. Using word probabilities as confidence measures. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing.Google ScholarGoogle Scholar
  35. Yang, A., Jafari, R., Sastry, S., and Bajcsy, R. 2009. Distributed recognition of human actions using wearable motion sensor networks. J. Ambient Intell. Smart Environ. 1, 1--5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Yoon, H., Lee, J., and Yang, H. 2002. An online signature verification system using hidden Markov model in polar space. In Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition. 329--333. Google ScholarGoogle ScholarDigital LibraryDigital Library

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