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.
- Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. 2002. Wireless sensor networks: a survey. Comput. Netw. 38, 4, 393--422. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- Bao, L. 2003. Physical activity recognition from acceleration data under semi-naturalistic conditions. Ph.D. dissutation, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- Figueiredo, M. and Jain, A. 2002. Unsupervised learning of finite mixture models. IEEE Tran. Pattern Anal. Mach. Intell. 24, 3, 381--396. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Hartigan, J. and Wong, M. 1979. A K-means clustering algorithm. JR Stat. Soc. Ser. C-Appl. Stat. 28, 100--108.Google Scholar
Cross Ref
- 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 Scholar
Cross Ref
- Johnson, S. 1967. Hierarchical clustering schemes. Psychometrika 32, 3, 241--254.Google Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Cross Ref
- Stoica, P. and Selen, Y. 2004. Model-order selection: A review of information criterion rules. IEEE Signal Process. Mag. 21, 4, 36--47.Google Scholar
Cross Ref
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
Index Terms
Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model
Recommendations
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
BSN '09: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor NetworksHuman movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into section based on the primary direction of motion. When analyzing a ...
A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors
Special section on body sensor networksHuman movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often ...
Fault Diagnosing ECG in Body Sensor Networks Based on Hidden Markov Model
MSN '14: Proceedings of the 2014 10th International Conference on Mobile Ad-hoc and Sensor NetworksIn this paper, we focus on medical body sensor networks collecting physiological signs to monitor the health of patients. We propose a Hidden Markov Model (HMM) based method for fault diagnosis of ECG sensor data. We firstly verify the Markov property ...






Comments