Privacy Preserving Automatic Fall Detection for Elderly Using RGBD Cameras

  • Chenyang Zhang
  • Yingli Tian
  • Elizabeth Capezuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7382)

Abstract

In this paper, we propose a new privacy preserving automatic fall detection method to facilitate the independence of older adults living in the community, reduce risks, and enhance the quality of life at home activities of daily living (ADLs) by using RGBD cameras. Our method can recognize 5 activities including standing, fall from standing, fall from chair, sit on chair, and sit on floor. The main analysis is based on the 3D depth information due to the advantages of handling illumination changes and identity protection. If the monitored person is out of the range of a 3D camera, RGB video is employed to continue the activity monitoring. Furthermore, we design a hierarchy classification schema to robustly recognize 5 activities. Experimental results on our database collected under conditions with normal lighting, without lighting, out of depth range demonstrate the effectiveness of the proposal method.

Keywords

Privacy Preserving Fall Detection Video Monitoring Elderly Activities of Daily Living 

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References

  1. 1.
    Hobbs, F.B.:The elderly population. In: U.S. Bureau of the Census, http://www.census.gov/population/www/pop-profile/elderpop.html
  2. 2.
    Brookmeyer, R., Gray, S., Kawas, C.: Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. American Journal of Public Health 88, 1337 (1998)CrossRefGoogle Scholar
  3. 3.
    Lee, H., Kim, Y.T., Jung, J.W., Park, K.H., Kim, D.J., Bang, B., Bien, Z.Z.: A 24-hour health monitoring system in a smart house. Gerontechnology 7, 22–35 (2008)Google Scholar
  4. 4.
    Wilson, D.H., Consolvo, S., Fishkin, K.P., Philipose, M.: Current practices for in-home monitoring of elders’ activities of daily living: A study of case managers. Citeseer (2005)Google Scholar
  5. 5.
    Nait-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: Proc. of International Conference on Pattern Recognition (ICPR), pp. 323–326. IEEE (2004)Google Scholar
  6. 6.
    Wang, S., Zabir, S., Leibe, B.: Lying Pose Recognition for Elderly Fall Detection. In: Proceedings of Robotics: Science and Systems, Los Angeles, CA, USA (2011)Google Scholar
  7. 7.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)Google Scholar
  8. 8.
    Buehler, P., Everingham, M., Huttenlocher, D.P., Zisserman, A.: Upper Body Detection and Tracking in Extended Signing Sequences. International Journal of Computer Vision (IJCV), 1–18 (2011)Google Scholar
  9. 9.
    Zhang, H., Parker, L.E.: 4-dimensional local spatio-temporal features for human activity recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2044–2049. IEEE (2011)Google Scholar
  10. 10.
    Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–14. IEEE (2010)Google Scholar
  11. 11.
    Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from RGBD images. In: AAAI Workshop on Pattern, Activity and Intent Recognition, PAIRW (2011)Google Scholar
  12. 12.
    Microsoft Research: Windows Kinect SDK Beta from Microsoft Research, Redmond WAGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chenyang Zhang
    • 1
  • Yingli Tian
    • 1
  • Elizabeth Capezuti
    • 2
  1. 1.Media LabThe City University of New York (CUNY), City CollegeNew YorkUSA
  2. 2.College of NursingNew York UniversityNew YorkUSA

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