Human Activity Recognition Framework in Monitored Environments

O. León, M. P. Cuellar, M. Delgado, Y. Le Borgne, G. Bontempi


This work addresses the problem of the recognition of human activities in Ambient Assisted Living (AAL) scenarios. The ultimate goal of a good AAL system is to learn and recognise behaviours or routines of the person or people living at home, in order to help them if something unusual happens. In this paper, we explore the advances in unobstrusive depth camera-based technologies to detect human activities involving motion. We explore the benefits of a framework for gesture recognition in this field, in contrast to raw signal processing techniques. For the framework validation, Hidden Markov Models and Dynamic Time Warping have been implemented for the action learning and recognition modules as a baseline due to their well known results in the field. The results obtained after the experimentation suggest that the depth sensors are accurate enough and useful in this field, and also that the preprocessing framework studied may result in a suitable methodology.


  1. (2007). Actions as Space-Time Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2247-2253.
  2. Azary, S. and Savakis, A. E. (2010). View invariant activity recognition with manifold learning. volume 6454 of Lecture Notes in Computer Science, pages 606-615. Springer.
  3. Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359-370. Seattle, WA.
  4. Corradini, A. (2001). Dynamic time warping for off-line recognition of a small gesture vocabulary. In Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on, pages 82-89.
  5. Crandall, A. S. and Cook, D. J. (2010). Using a hidden markov model for resident identification. In Proceedings of the 6th Int. Conf. on Intelligent Environments, IE 7810, pages 74-79.
  6. Gao, Q. and Sun, S. (2013a). Human activity recognition with beta process hidden markov models. In Proceedings of the International Conference on Machine Learning and Cybernetics, pages 1-6.
  7. Gao, Q. and Sun, S. (2013b). Trajectory-based human activity recognition with hierarchical dirichlet process hidden markov models. In Proceedings of the 1st IEEE China Summit and International Conference on Signal and Information Processing, pages 1-5.
  8. Giles, J. (2010). Inside the race to hack the kinect. New Scientist, 208(2789):22-23.
  9. Hein, A. and Kirste, T. (2008). Activity recognition for ambient assisted living : Potential and challenges. Sensors Peterborough NH, pages 263-268.
  10. Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2001). Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems, 3(3):263-286.
  11. Laptev, I. (2005). On space-time interest points. Int. J. Comput. Vision, 64:107-123.
  12. Minhas, R., Baradarani, A., Seifzadeh, S., and Jonathan Wu, Q. M. (2010). Human action recognition using extreme learning machine based on visual vocabularies. Neurocomput., 73:1906-1917.
  13. Mitsa, T. (2010). Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series). Chapman and Hall/CRC.
  14. Mokhber, A., Achard, C., and Milgram, M. (2008). Recognition of human behavior by space-time silhouette characterization. Pattern Recognition Letters, 29(1):81-89.
  15. Rabiner, L. and Juang, B. (2003). An introduction to hidden Markov models. ASSP Magazine, IEEE, 3(1):4-16.
  16. Raheja, J. L., Chaudhary, A., and Singal, K. (2011). Tracking of fingertips and centers of palm using kinect. In Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on, pages 248-252. IEEE.
  17. Rantz, M., A., G., A., Oliver, D., M., M., S., J., K., Z., H., M., P., G., D., and S., M. (2008). An innovative educational and research environment. volume 17, page 8491.
  18. Reifinger, S., Wallhoff, F., Ablassmeier, M., Poitschke, T., and Rigoll, G. (2007). Static and dynamic handgesture recognition for augmented reality applications. In Int. Conf. on Human-computer interaction, HCI'07, pages 728-737. Springer-Verlag.
  19. Salas, J. and Tomasi, C. (2011). People detection using color and depth images. In Proc. of the 3rd Mexican conference on Pattern recognition, MCPR'11, pages 127-135. Springer-Verlag.
  20. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., and Moore, R. (2013). Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56(1):116-124.
  21. Storf, H., Becker, M., and Riedl, M. (2009). Rule-based activity recognition framework: Challenges, technique and learning. In Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on, pages 1-7.
  22. Xia, L., Chen, C.-C., and Aggarwal, J. K. (2012). View invariant human action recognition using histograms of 3d joints. In CVPR Workshops, pages 20-27. IEEE.
  23. Yang, X. and Tian, Y. (2012). Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 14-19. IEEE.
  24. Yang, X. and Tian, Y. (2013). Effective 3d action recognition using eigenjoints. Journal of Visual Communication and Image Representation.
  25. Yang, X., Zhang, C., and Tian, Y. (2012). Recognizing actions using depth motion maps-based histograms of oriented gradients. In Babaguchi, N., Aizawa, K., Smith, J. R., Satoh, S., Plagemann, T., Hua, X.-S., and Yan, R., editors, ACM Multimedia, pages 1057-1060. ACM.

Paper Citation

in Harvard Style

León O., P. Cuellar M., Delgado M., Le Borgne Y. and Bontempi G. (2014). Human Activity Recognition Framework in Monitored Environments . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 487-494. DOI: 10.5220/0004755504870494

in Bibtex Style

author={O. León and M. P. Cuellar and M. Delgado and Y. Le Borgne and G. Bontempi},
title={Human Activity Recognition Framework in Monitored Environments},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Human Activity Recognition Framework in Monitored Environments
SN - 978-989-758-018-5
AU - León O.
AU - P. Cuellar M.
AU - Delgado M.
AU - Le Borgne Y.
AU - Bontempi G.
PY - 2014
SP - 487
EP - 494
DO - 10.5220/0004755504870494