Movement Recognition

Movement recognition in the Blackbox

The Blackbox currently supports Laban Basic Effort recognition through the EffortDetect system. See also the discussion below on LMA recognition.

Movement recognition outside the Blackbox

In general, a variety of heuristic techniques are applicable to movement information derived from sensor data. The following techniques below represent some that have been reported in the literature:

• Frequency-domain analysis (Yang & Hsu, 2010)
  • Analysis of variance
  • Analysis of frequency peaks
  • Discrete wavelet transform(Sekine, Tamura, Togawa, & Fukui, 2000)
  • Signal magnitude area (Karantonis, Narayanan, M. Mathie, Lovell, & Celler, 2006)
• Statistical approaches (Yang & Hsu, 2010)
  • Decision trees (M. J. Mathie, Celler, Lovell, & Coster, 2004)
  • k-nearest neighbor
  • support vector machines
  • Naïve Bayes classifier
  • Gaussian mixture model
  • Hidden Markov models
  • Dynamic Conditional Random Field (Morency, Quattoni, & Darrell, 2007)
  • Boltzmann machines (Taylor & Hinton, 2009)

(Note: Dynamic conditional random fields and Boltzmann machines were suggested by AAAI reviewers for any future versions of EffortDetect.)

References

Rett, J., Dias, J., & Ahuactzin, J. M. (2008). Laban Movement Analysis using a Bayesian model and perspective projections. In C. Rossi (Ed.), Brain, Vision and AI (pp. 183-210). Vienna: InTech Education and Publishing.

Rett, J., Santos, L., & Dias, J. (2008). Laban Movement Analysis for multi-ocular systems. Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on (pp. 761-766). Presented at the Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on.

Rett, Jorg, & Dias, Jorge. (2007a). Bayesian models for Laban Movement Analysis used in human machine interaction. Proceedings of ICRA 2007 Workshop on �Concept Learning for Embodied Agents.� Retrieved from http://paloma.isr.uc.pt/pub/bscw.cgi/d56530/Paper@ICRA07-WS.pdf

Rett, Jorg, & Dias, Jorge. (2007b). Human-robot interface with anticipatory characteristics based on Laban Movement Analysis and Bayesian models. 2007 IEEE 10th International Conference on Rehabilitation Robotics (pp. 257-268). Presented at the 2007 IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, Netherlands. doi:10.1109/ICORR.2007.4428436

Santos, L., Prado, J. A., & Dias, J. (2009). Human Robot interaction studies on Laban human movement analysis and dynamic background segmentation (pp. 4984-4989). Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2009, St. Louis, MO. doi:10.1109/IROS.2009.5354564

Subyen, P., Maranan, D. S., Schiphorst, T., Pasquier, P., & Bartram, L. (2011). EMVIZ: The Poetics of Movement Quality Visualization. Proceedings of Computational Aesthetic 2011 Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging. Presented at the Computational Aesthetics, Vancouver, Canada.

Swaminathan, D., Thornburg, H., Mumford, J., Rajko, S., James, J., Ingalls, T., Campana, E., et al. (2009). A dynamic Bayesian approach to computational Laban shape quality analysis. Advances in Human Computer Interaciton, 2009, 1-17.

Zhao, L. (2001). Synthesis and acquisition of laban movement analysis qualitative parameters for communicative gestures (Ph.D. dissertation). University of Pennsylvania, Philadelphia, PA, USA.

Zhao, Liwei, & Badler, N. (2005). Acquiring and validating motion qualities from live limb gestures. Graphical Models, 67(1), 1-16. doi:10.1016/j.gmod.2004.08.002