Laban Movement Analysis towards Behavior Patterns

  • Luís Santos
  • Jorge Dias
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 314)


This work presents a study about the use of Laban Movement Analysis (LMA) as a robust tool to describe human basic behavior patterns, to be applied in human-machine interaction. LMA is a language used to describe and annotate dancing movements and is divided in components [1]: Body, Space, Shape and Effort. Despite its general framework is widely used in physical and mental therapy [2], it has found little application in the engineering domain. Rett J. [3] proposed to implement LMA using Bayesian Networks. However LMA component models have not yet been fully implemented. A study on how to approach behavior using LMA is presented. Behavior is a complex feature and movement chain, but we believe that most basic behavior primitives can be discretized in simple features. Correctly identifying Laban parameters and the movements the authors feel that good patterns can be found within a specific set of basic behavior semantics.


Laban Movement Analysis Behavior Patterns Bayesian Networks Movement Characterization 


  1. 1.
    Zhao, L.: Synthesis and Acquisition of Laban Movement Analysis Qualitative Parameters for Communicative Gestures. PhD Thesis, University of Pennsylvania (2002)Google Scholar
  2. 2.
    Bartenieff, I., Lewis, D.: Body Movement: Coping with the Environment. Gordon and Breach Science, New York (1980)Google Scholar
  3. 3.
    Rett, J.: Robot Human Interface Using Laban Movement Analysis Inside a Bayesian Framework. PhD Thesis, University of Coimbra (2009)Google Scholar
  4. 4.
    Rett, J., Santos, L., Dias, J.: Laban Movement Analysis using Multi-Ocular System. In: International Conference on Intelligent Robots and Systems, IROS (2008)Google Scholar
  5. 5.
    Prado, J., Santos, L., Dias, J.: Horopter based Dynamic Background Segmentation applied to an Interactive Mobile Robot. In: 14th International Conference on Advanced Robotics, ICAR (2009)Google Scholar
  6. 6.
    Foroud, A., Whishaw, I.Q.: Changes in the kinematic structure and non-kinematic features of movements during skilled reaching after stroke: A Laban movement analysis in two case studies. Journal of Neuroscience Methods 158, 137–149 (2006)CrossRefGoogle Scholar
  7. 7.
    Chi, D., Costa, M., Zhao, L., Badler, N.: The emote model for effort and shape. In: SIGGRAPH 2000, Computer Graphics Proceedings. Annual Conference Series, ACM SIGGRAPH, pp. 173–182. ACM Press, New York (2000)Google Scholar
  8. 8.
    Hongeng, S., Nevatia, R., Bremond, F.: Video-based event recognition: activity representation and probabilistic recognition methods. Computer Vision and Image Understanding 96, 129–162 (2004)CrossRefGoogle Scholar
  9. 9.
    Medioni, G., Cohen, I., Bremond, F., Hongeng, S., Nevatia, R.: Event detection and analysis from video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 873–889 (2001)CrossRefGoogle Scholar
  10. 10.
    Arsic, D., Wallhoff, F., Schuller, B., Rigoll, G.: Video based online behavior detection using probabilistic multi-stream fusion. In: Proceedings of the IEEE International Conference on Image Processing, vol. 2, pp. 606–609 (2005)Google Scholar
  11. 11.
    León, R.D., Sucar, L.E.: Continuous activity recognition with missing data. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 1, pp. 439–442 (2002)Google Scholar
  12. 12.
    Santos, L., Prado, J., Dias, J.: Human Robot Interaction Studies on Laban Human Movement Analysis and Dynamic Background Segmentation. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Luís Santos
    • 1
  • Jorge Dias
    • 1
  1. 1.Instituto de Sistemas e Robótica, Departamento de Engenharia Electrotécnica e de ComputadoresFaculdade de Ciência e Tecnologia da Universidade de CoimbraCoimbraPortugal

Personalised recommendations