Intelligent Spaces as Assistive Environments: Visual Fall Detection Using an Evolutive Algorithm

  • José María Cañas
  • Sara Marugán
  • Marta Marrón
  • Juan C. García
Part of the Studies in Computational Intelligence book series (SCI, volume 372)


Artificial vision provides a remarkable good sensor when developing applications for intelligent spaces. Cameras are passive sensors that supply a great amount of information and are quite cheap. This chapter presents an application for elderly care that detects falls or faints and automatically triggers the health alarm. It promotes the independent lifestyle of elder people at their homes as the monitoring application will call for timely health assistance when needed. The system extracts 3D information from several cameras and performs 3D tracking of the people in the intelligent space. One evolutive multimodal algorithm has been developed to continuously estimate the 3D positions in real time of several persons moving in the monitored area. It is based on 3D points and learns the visual appearance of the persons and uses colour and movement as tracking cues. The system has been validated with some experiments in different real environments.


detection vision fall three-dimensional eldercare 


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  1. 1.
    Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A Smart and Passive Floor-Vibration Based Fall Detector for Elderly. In: The 2nd IEEE International Conference on Information & Communication Technologies: from Theory to Applications - ICTTA 2006, April 24 - 28, Damascus, Syria (2006)Google Scholar
  2. 2.
    Barrera, P., Cañas, J., Matellán, V.: Visual object tracking in 3D with color based particle filter. Int. Journal of Information Technology 2(1), 61–65 (2005)Google Scholar
  3. 3.
    Brownsell, S., Hawley, M.S.: Fall monitoring. In: Wootton, R., Dimmicky, S., Kvedar, J. (eds.) Home telehealth: connecting care within the community, pp. 108–120. Royal Society of Medicine Press (2006)Google Scholar
  4. 4.
    Fritsch, J., Kleinehagenbrock, M., Lang, S., Fink, G., Sagerer, G.: Audiovisual person tracking with a mobile robot. In: Proceedings of Int. Conf. on Intelligent Autonomous Systems, pp. 898–906 (2004)Google Scholar
  5. 5.
    Huang, C.L., Chen, E.L., Chung, P.C.: Fall Detection using Modular Neural Networks and Back-projected Optical Flow. Biomedical Engineering - Applications, Basis and Communications 19(6), 415–424 (2007)CrossRefGoogle Scholar
  6. 6.
    Louchet, J.: Stereo analysis using individual evolution strategy. In: Proceedings of the 15th International Conference on Pattern Recognition, pp. 908–911 (2001)Google Scholar
  7. 7.
    Louchet, J.: Using an individual evolution strategy for stereovision. Genetic Programming and Evolvable Machines (2001)Google Scholar
  8. 8.
    Louchet, J., Guyon, M., Lesot, M.J., Boumaza, A.: Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern recognition letters (2002)Google Scholar
  9. 9.
    Miaou, S.G., Shih, F.C., Huang, C.Y.: A Smart Vision-based Human Fall Detection System for Telehealth Applications. In: Bashshur, R. (ed.) Proc.Third ISATED Int. Conf. on Telehealth, Montreal, Canada, pp. 7–12. Acta Press (2007)Google Scholar
  10. 10.
    Pérez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. Proceedings of IEEE 92(3), 495–513 (2004)CrossRefGoogle Scholar
  11. 11.
    Pupilli, M., Calway, A.: Real-Time Camera tracking using a particle filter. In: Proceedings of British Machine Vision Conference, pp. 519–528 (2005)Google Scholar
  12. 12.
    Rajendran, P., Corcoran, A., Kinosian, B., Alwan, M.: Falls, Fall Prevention, and Fall Detection Technologies. In: Eldercare Technology for Clinical Practitioners, pp. 187–202. Humana Press (2008)Google Scholar
  13. 13.
    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall Detection from Human Shape and Motion History Using Video Surveillance. In: 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW 2007), vol. 2, pp. 875–880 (2007)Google Scholar
  14. 14.
    Sixmith, A., Johnson, N.: A smart sensor to detect the falls of the elderly. Pervasive Computing 3(2), 42–47 (2004)CrossRefGoogle Scholar
  15. 15.
    Yang, C.C., Hsu, Y.L.: Developing a Wearable System for Real-Time Physical Activity Monitoring in a Home Environment. In: Bashshur, R. (ed.) Proc. Third ISATED Int. Conf. on Telehealth, Montreal, Canada. Acta Press (2007)Google Scholar
  16. 16.
    Yiping, T., Shunjing, T., Zhongyuan, Y., Sisi, Y.: Detection Elder Abnormal Activities by using omni-directional vision sensor: activity data collection and modeling. In: Proc. Int. Joint Conference SICE-ICASE, pp. 3850–3853 (2006)Google Scholar
  17. 17.
    Zotkin, D., Duraiswami, R., Davis, L.: Multimodal 3D tracking and event detection via the particle filter. In: IEEE Workshop on Detection and Recognition of Events in Video, pp. 20–27 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José María Cañas
    • 1
  • Sara Marugán
    • 1
  • Marta Marrón
    • 2
  • Juan C. García
    • 2
  1. 1.Universidad Rey Juan CarlosSpain
  2. 2.Universidad de AlcaláSpain

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