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Intelligent Decision Technologies pp 215–224Cite as

Fuzzy Decision Making Model for Human Fall Detection and Inactivity Monitoring

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 15))

Abstract

In this paper a fuzzy decision making model for fall detection and inactivity monitoring is presented. The inputs to the model are regions of interest (ROIs) corresponding to humans segmented in infrared video. The classification features proposed in fuzzy decision include geometric and kinematic parameters associated with more or less sudden changes in the tracked bounding boxes of the ROIs. The paper introduces a complete fuzzy-based fall detection system capable of identifying true and false falls, enhanced with inactivity monitoring aimed at confirming the need for medical assistance and/or care. The fall indicators used as well as their fuzzy model is explained in detail. As the proposed model has been tested for a wide number of static and dynamic falls, some exciting initial results are presented here.

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References

  1. Anderson, D., Keller, J.M., Skubic, M., Chen, X., He, Z.: Recognizing falls from silhouettes. In: Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, pp. 6388–6391 (2006)

    Google Scholar 

  2. Castillo, J.C., Serrano-Cuerda, J., Fernández-Caballero, A.: Robust People Segmentation by Static Infrared Surveillance Camera. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010. LNCS, vol. 6096, pp. 348–357. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Chang, S.-L., Yang, F.-T., Wu, W.-P., Cho, Y.-A., Chen, S.-W.: Nighttime pedestrian detection using thermal imaging based on HOG feature. In: Sokolova, M.V., Fernández-Caballero, A. (eds.) International Conference on System Science and Engineering, pp. 694–698 (2011)

    Google Scholar 

  4. Davis, J.W., Sharma, V.: Robust detection of people in thermal imagery. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 4, pp. 713–716 (2004)

    Google Scholar 

  5. Doukas, C., Maglogiannis, I.: Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Transactions on Information Technology in Biomedicine 15(2), 277–289 (2011)

    Article  Google Scholar 

  6. Escobet, A., Nebot, A., Cellier, F.E.: Fault diagnosis system based on fuzzy logic: Application to a valve actuator benchmark. Journal of Intelligent and Fuzzy Systems 22(4), 155–171 (2011)

    Google Scholar 

  7. Fang, Y., Yamada, K., Ninomiya, Y., Horn, B.K.P., Masaki, I.: A shape-independent method for pedestrian detection with far-infrared images. IEEE Transactions on Vehicular Technology 53(6), 1679–1697 (2004)

    Article  Google Scholar 

  8. Fernández-Caballero, A., Castillo, J.C., Serrano-Cuerda, J., Maldonado-Bascón, S.: Real-time human segmentation in infrared videos. Expert Systems with Applications 38(3), 2577–2584 (2011)

    Article  Google Scholar 

  9. Fernández-Caballero, A., Castillo, J.C., Martínez-Cantos, J., Martínez-Tomás, R.: Optical flow or image subtraction in human detection from infrared camera on mobile robot. Robotics and Autonomous Systems 58(12), 1273–1281 (2010)

    Article  Google Scholar 

  10. Khawandi, S., Daya, B., Chauvet, P.: Implementation of a monitoring system for fall detection in elderly healthcare. Procedia Computer Science 3, 216–220 (2011)

    Article  Google Scholar 

  11. Li, J., Gong, W., Li, W., Liu, X.: Robust pedestrian detection in thermal infrared imagery using the wavelet transform. Infrared Physics and Technology 53(4), 267–273 (2010)

    Article  Google Scholar 

  12. Liu, C.-L., Lee, C.-H., Lin, P.-M.: A fall detection system using k-nearest neighbor classifier. Expert Systems with Applications 37(10), 7174–7181 (2010)

    Article  Google Scholar 

  13. Loomis, A.: Figure Drawing for All it’s Worth, Viking Adult (1943)

    Google Scholar 

  14. Mo, H.-C., Leou, J.-J., Lin, C.-S.: Human behavior analysis using multiple 2d features and multicategory support vector machine. In: Proceedings of the IAPR Conference on Machine Vision Applications, pp. 46–49 (2009)

    Google Scholar 

  15. Moon, H., Chellappa, R., Rosenfeld, A.: Tracking of human activities using shapeencoded particle propagation. In: Processing of the International Conference on Image Processing, vol. 1, pp. 357–360 (2001)

    Google Scholar 

  16. Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., López, M.T.: Video sequence motion tracking by fuzzification techniques. Applied Soft Computing 10(1), 318–331 (2010)

    Article  Google Scholar 

  17. Otsu, N.: A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  18. Rougier, C., Meunier, J.: Demo: fall detection using 3d head trajectory extracted from a single camera video sequence. In: Proceedings of the First International Workshop on Video Processing for Security (2006)

    Google Scholar 

  19. Schmidt, J., Fritsch, J., Kwolek, B.: Kernel particle filter for real-time 3d body tracking in monocular color images. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, pp. 567–572 (2006)

    Google Scholar 

  20. Spark! Fuzzy Logic Editor (2011), http://www.louderthanabomb.com/spark.htm

  21. Tan, S.C., Lim, C.P.: Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification. Journal of Intelligent and Fuzzy Systems 22(2), 57–68 (2011)

    MathSciNet  Google Scholar 

  22. Wei, X., Chai, J.: Modeling 3d human poses from uncalibrated monocular images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1873–1880 (2009)

    Google Scholar 

  23. Yu, M., Rhuma, A., Naqvi, S.M., Chambers, J.: Fall detection for the elderly in a smart room by using an enhanced one class support vector machine. In: Proceedings of the 17th International Conference on Digital Signal Processing, pp. 1–6 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    Marina V. Sokolova

  2. South-West State University, 94, 50 let Oktyabrya, 305040, Kursk, Russia

    Marina V. Sokolova

  3. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    Antonio Fernández-Caballero

Authors
  1. Marina V. Sokolova
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  2. Antonio Fernández-Caballero
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Corresponding author

Correspondence to Marina V. Sokolova .

Editor information

Editors and Affiliations

  1. Graduate School of Information,, Production & Systems (IPS), Waseda University, Hibikino 2-7, Kitakyushu, 808-0135, Japan

    Junzo Watada

  2. Graduate School of Information Science, Nagoya University, Furo-cho, Nagoya, 464-8601, Japan

    Toyohide Watanabe

  3. Sellinger School of Business and, Management, Loyola University Maryland, Charles Street 4501 N, Baltimore, 21210, USA

    Gloria Phillips-Wren

  4. KES International, PO Box 2115, Shoreham-by-Sea, BN43 9AF, United Kingdom

    Robert J. Howlett

  5. , School of Electrical and Information, University of South Australia, 5095, Adelaide, Australia

    Lakhmi C. Jain

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© 2012 Springer-Verlag Berlin Heidelberg

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Sokolova, M.V., Fernández-Caballero, A. (2012). Fuzzy Decision Making Model for Human Fall Detection and Inactivity Monitoring. In: Watada, J., Watanabe, T., Phillips-Wren, G., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29977-3_22

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  • DOI: https://doi.org/10.1007/978-3-642-29977-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29976-6

  • Online ISBN: 978-3-642-29977-3

  • eBook Packages: EngineeringEngineering (R0)

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