Range Image Processing for Real Time Hospital-Room Monitoring

  • Alessandro Mecocci
  • Francesco MicheliEmail author
  • Claudia Zoppetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


In this paper we describe a robust and movable real-time system, based on range data and 2D image processing, to monitor hospital-rooms and to provide useful information that can be used to give early warnings in case of dangerous situations. The system auto-configures itself in real-time, no initial supervised setup is necessary, so is easy to displace it from room to room, according to the effective hospital needs. Night-and-day operations are granted even in presence of severe occlusions, by exploiting the 3D data given by a Kinect\(^\copyright \) sensor. High performance is obtained by a hierarchical approach that first detects the rough geometry of the scene. Thereafter, the system detects the other entities, like beds and people. The current implementation has been preliminarily tested at “Le Scotte” polyclinic hospital in Siena, and allows a 24 h coverage of up to three beds by a single Kinect\(^\copyright \) in a typical room.


Support Vector Machine Point Cloud Floor Plane Fall Detection Bottom Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Wolf, M., Alexander, B., Rivara, F.: The cost and frequency of hospitalization for fall related injuries in older adults. Am. J. Public Health 82, 1020–1023 (1992)CrossRefGoogle Scholar
  2. 2.
    Hennessy, A., Bell, A., Talbot-Stern, J.: Characteristics and outcomes of older patients presenting to the emergency department after a fall: a retrospective analysis. Med. J. Aust. 173, 176 (2000)Google Scholar
  3. 3.
    Finkelstein, E., Miller, T., Stevens, J., Corso, P.: The costs of fatal and nonfatal falls among older adults. Inj. Prev. 12, 290 (2006)CrossRefGoogle Scholar
  4. 4.
    Skubic, M., Stone, E.: Silhouette classification using pixel and voxel features for improved elder monitoring in dynamic environments. In: Workshop on Smart Environments to Enhance Health Care, Seattle, USA (2011)Google Scholar
  5. 5.
    Li, M., Popescu, M., Stone, E., Skubic, M., Banerjee, T., Rantz, M., Scott, S.: Monitoring hospital rooms for safety using depth images. In: AI for Gerontechnology, Arlington, Virginia (2012)Google Scholar
  6. 6.
    Fosty, B., Konig, A., Romdhane, R., Thonnat, M., Crispim Junior, C., Bathrinarayana, V., Bremond, F.: Evaluation of a monitoring system for event recognition of older people. In: International Conference on Advanced Video and Signal-Based Surveillance, pp. 4321–4326 (2013)Google Scholar
  7. 7.
    Gasperini, S., Cippitelli, E.: A depth-based fall detecion system using a kinect sensor. Sensor 14, 2756–2775 (2014)CrossRefGoogle Scholar
  8. 8.
    Brumitt, B., Toyama, K., Krumm, J., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 255–261 (1999)Google Scholar
  9. 9.
    Grimson, W., Stauffer, C.: Learning patterns of activity using real time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22, 747–757 (2000)CrossRefGoogle Scholar
  10. 10.
    Ganesan, D., Williams, A., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th International Conference on Multimedia, pp. 892–901 (2007)Google Scholar
  11. 11.
    Kawatsu, C., Li, J., Chung, C.J.: Development of a fall detection system with microsoft kinect. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications. AISC, vol. 208, pp. 623–630. Springer, Heidelberg (2013) Google Scholar
  12. 12.
    Zhang, C., Tian, Y.: Rgb-d camera-based daily living activity recognition. J. Comput. Vis. Image Process. 2, 12 (2012)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Rusu, R., Cousins, S.: 3d is here: point cloud library. In: ICRA (2011)Google Scholar
  17. 17.
    Lingemann, K., Borrmann, D., Elseberg, J., Nuchter, A.: The 3D hough transform for plane detection in point clouds - a review and a new accumulator design. 3D Res. 2, 1–13 (2011)Google Scholar
  18. 18.
    Guan, L., Yu, T., Tu, P., Lim, S.: Simultaneous image segmentation and 3D plane fitting for rgb-d sensors. an iterative framework. In: IEEE Computer Vision and Pattern Recognition Workshop (CVPRW) (2012)Google Scholar
  19. 19.
    Cowley, A., Taylor, C.: Segmentation and analysis of rgb-d data. In: RSS 2011 Workshop on RGB-D Cameras (2011)Google Scholar
  20. 20.
    Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: Real-time plane segmentation using RGB-D cameras. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS, vol. 7416, pp. 306–317. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  21. 21.
    Triebel, R., Burgard, W.: Using hierarchical EM to extract planes from 3d range scans. In: Proceedings of the IEEE International Conference on Robotics & Automation (ICRA) (2005)Google Scholar
  22. 22.
    Harville, M.: Stereo person tracking with adaptive plan-view statistical templates. In: ECCV Workshop on Statistical Methods in Video Processing (SMVP) (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alessandro Mecocci
    • 1
  • Francesco Micheli
    • 1
    Email author
  • Claudia Zoppetti
    • 1
  1. 1.Department of Information Engineering and MathematicsUniversity of SienaSienaItaly

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