Multi-Sensor Signal based Situation Recognition with Bayesian Networks

  • Kim, Jin-Pyung (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Jang, Gyu-Jin (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Jung, Jae-Young (Dept. of Computer and Information Warfare, Dongyang University) ;
  • Kim, Moon-Hyun (College of Information and Communication Engineering, Sungkyunkwan University)
  • Received : 2013.11.07
  • Accepted : 2013.12.30
  • Published : 2014.05.01


In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.


Structure learning;Bayesian networks;Multiple sensor signals;K2-learning algorithm;UCI machine learning repository


  1. David L. Hall, James Llinas, "Multisensor Data Fusion", ISBN 0-8493-2379-7, 2001, CRC Press LLC
  2. R. C. Baker and B. Charlie, "Planning and acting in partially observable stochastic domains", Artificial Intelligence, Volume 101, Issues 1-2, pp. 99-134, 1998.
  3. Lerner, Boaz, and Roy Malka*, "Investigation of the K2 algorithm in learning Bayesian network classifiers.", Applied Artificial Intelligence 25.1 (2011): 74-96.
  4. Chen, Xue-Wen, Gopalakrishna Anantha, and Xiaotong Lin, "Improving Bayesian network structure learning with mutual information-based node ordering in the K2 algorithm.", Knowledge and Data Engineering, IEEE Transactions on 20.5 (2008): 628-640.
  5. Evelina Lamma, Fabrizio Riguzzi and Sergio Storari, "improving the K2 Algorithm Using Association Rule Parameters", Modern Information Processing, 207-217, 2006A.
  6. Gregory F. Cooper and Edward Herskovits, "A Bayesian method for the induction of probabilistic networks from data", MACHINE LEARNING, Volume 9, Number 4, 309-347,1992.
  7. J. Ramon and L. De Raedt, "Multi instance neural networks", Workshop on Attribute-Value and Relational Learning, In Proceedings of ICML-2000, 2000.
  8. Michael Buettner, Richa Prasad, Matthai Philipose, David Wetherall, "Recognizing Daily Activities with RFID-Based Sensors", ACM International Joint Conference on Pervasive and Ubiquitous Computing, (2009), 51-60.
  9. B. Horling, R. Vincent, R. Mailler, J. Shen, R. Becker, K. Rawlins, V. Lesser, "Distributed Sensor Network for Real Time Tracking", Proceedings of the 5th Inter-national Conference on Autonomous Agents (2001) 417-424.
  10. UCI repository of machine learning databases, Robot Execution Failures Data Set
  11. Lopes, Luis Seabra, and Luis M. Camarinha-Matos. "Feature transformation strategies for a robot learning problem." Feature Extraction, Construction and Selection, Springer US, 1998. 375-391.
  12. Guoqiang Peter Zhang, "Neural Networks for Classification: A Survey", IEEE transactions on systems, man, and cybernetics, Vol. 30, No. 4, November 2000.
  13. Daniel Roggen, Martin Wirz and Gerhard Troster, "Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods", Networks and Heterogeneous Media, American Institute of Mathematical Sciences, Volume 6, Number 3, September 2011, pp. 521-544
  14. K. Bao and S. Intille, "Activity Recognition from User-Annotated Acceleration Data", Proc 2nd Int Conf Pervasive Computing, (2004), 1-17.
  15. J. A.Ward, P. Lukowicz and H. Gellersen, "Performance metrics for activity recognition", ACM Transactions on Information Systems and Technology, 2 (2011), 6:1-6:23.
  16. Christopher R. Wren, Emmanuel Munguia Tapia, "Toward scalable activity recognition for sensor networks", Lecture Note in Computer Science, Springer-Verlag, pp. 168-185, 2006.
  17. R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, D. Estrin, "Habitat monitoring with sensor networks", Communications of the ACM 47(6) (2004) 34-40.
  18. Tae-Ki An, Moon-Hyun Kim, "Context-Aware Video Surveillance System", Journal of Electrical Engineering & Technology, Volume 7, Number 1, 115-123, 2012.
  19. P. Langley, W. Iba, K. Thompson, "An analysis of bayesian classifiers", in: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI Press, San Jose, CA, 1992.
  20. K. Y. Eom, J. Y. Jung, and M. H. Kim, "A heuristic search-based motion correspondence algorithm using fuzzy clustering", International Journal of Control, Automation and Systems, vol. 10, no. 3, pp. 594-602, 2012.
  21. XIE, Xiaohui, et al. "Method and apparatus for identifying a gesture based upon fusion of multiple sensor signals." WIPO Patent No. 2013082806. 14 Jun. 2013.
  22. Gold, Ben, Nelson Morgan, and Dan Ellis. "Statistical Pattern Classification." Speech and Audio Signal Processing: Processing and Perception of Speech and Music, Second Edition (2011): 124-138.

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