Detection of Abnormal Gait from Skeleton Data

Meng Meng, Hassen Drira, Mohamed Daoudi, Jacques Boonaert

Abstract

Human gait analysis has becomes of special interest to computer vision community in recent years. The recently developed commodity depth sensors bring new opportunities in this domain.In this paper, we study the human gait using non intrusive sensors (Kinect 2) in order to classify normal human gait and abnormal ones. We propose the evolution of inter-joints distances as spatio temporal intrinsic feature that have the advantage to be robust to location. We achieve 98% success to classify normal and abnormal gaits and show some relevant features that are able to distinguish them.

References

  1. A. A. Chaaraoui, P. C.-P. and Flrez-Revuelta, F. (2012). A review on vision techniques applied to human behaviour analysis for ambient-assisted living. In Expert Systems with Applications, 39(12):1087310888.
  2. A. Paiement, L. Tao, S. H., Camplani, M., Damen, D., and M.Mirmehdi (2014). Online quality assessment of human movement from skeleton data. In Proceedings of British Machine Vision Conference (BMVC).
  3. Breiman, L. (2001). Random forests. In Machine Learning, vol. 45, pp 5-32.
  4. C. Alexandros, J. P.-L. and Flórez-Revuelta, F. (2015). Abnormal gait detection with rgb-d devices using joint motion history features.
  5. Dian, G. and Medioni, G. (2011). Dynamic manifold warping for view invariant action recognition. In IEEE International Conference on Computer Vision (ICCV).
  6. G. S. Parra-Dominguez, B. T. and Mihailidis, A. (2012). 3d human motion analysis to detect abnormal events on stairs. In In International Conference on 3D Imaging, Modeling,Processing, Visualization and Transmission, pages 97103.
  7. J. Shotton, A. Fitzgibbo, M. C., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (June 2011). Real-time human pose recognition in parts from single depth images. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition.
  8. J. Snoek, J. Hoey, L. S., Zemel, R. S., and Mihailidis, A. (2009). Automated detection of unusual events on stairs. In Image and Vision Computing, vol. 27, no. 1-2, pp. 153 166,.
  9. M. Devanne, H. Wannous, S. B., Daoudi, M., and Bimbo, A. (2013). Space-time pose representation for 3d human action recognition. In New Trends in Image Analysis and Processing ICIAP 2013.
  10. Omar, O. and Liu, Z. (2013). Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  11. R. Slama, H. Wannous, M. D. and Srivastava, A. (2015). Accurate 3d action recognition using learning on the grassmann manifold. In Pattern Recognition 48.2 : 556-567.
  12. W. Li, Z. Z. and Liu, Z. (2010). Action recognition based on a bag of 3d points. In IEEE Computer Vision and Pattern Recognition Workshops (CVPRW)).
Download


Paper Citation


in Harvard Style

Meng M., Drira H., Daoudi M. and Boonaert J. (2016). Detection of Abnormal Gait from Skeleton Data . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 131-137. DOI: 10.5220/0005722901310137


in Bibtex Style

@conference{visapp16,
author={Meng Meng and Hassen Drira and Mohamed Daoudi and Jacques Boonaert},
title={Detection of Abnormal Gait from Skeleton Data},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={131-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005722901310137},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Detection of Abnormal Gait from Skeleton Data
SN - 978-989-758-175-5
AU - Meng M.
AU - Drira H.
AU - Daoudi M.
AU - Boonaert J.
PY - 2016
SP - 131
EP - 137
DO - 10.5220/0005722901310137