Composition of Dynamical Systems for Estimation of Human Body Dynamics

  • Sumitra Ganesh
  • Aaron D. Ames
  • Ruzena Bajcsy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4416)


This paper addresses the problem of estimating human body dynamics from 3-D visual data. That is, our goal is to estimate the state of the system, joint angle trajectories and velocities, and the control required to produce the observed motion from indirect noisy measurements of the joint angles. For a two-link chain in the human body, we show how two independent spherical pendulums can be composed to create a behaviorally equivalent double spherical pendulum. Therefore, the estimation problem can be solved in parallel for the low-dimensional spherical pendulum systems and the composition result can be used to arrive at estimates for the higher dimensional double spherical pendulum system. We demonstrate our methods on motion capture data of human arm motion.


Joint Angle Open Chain Motion Capture Data Composition Result Auxiliary Particle 
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  1. 1.
    Bregler, C., Malik, J.: Learning and recognizing human dynamics in video sequences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 568–674. IEEE Computer Society Press, Los Alamitos (1997)Google Scholar
  2. 2.
    Del Vecchio, D., Murray, R., Perona, P.: Decomposition of human motion into dynamics based primitives with application to drawing tasks. Automatica 39(12), 2085–2098 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Murray, R., Sastry, S., Li, Z.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)zbMATHGoogle Scholar
  4. 4.
    Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, Chichester (1990)Google Scholar
  5. 5.
    Pitt, M.K., Shephard, N.: Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association 94(446), 590–599 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Lewis, F., Syrmos, V.: Optimal Control. Wiley-Interscience, Chichester (1995)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Sumitra Ganesh
    • 1
  • Aaron D. Ames
    • 2
  • Ruzena Bajcsy
    • 1
  1. 1.Department of Electrical Engineering and Computer Sciences University of California, Berkeley, CA 94720 
  2. 2.Control and Dynamical Systems California Institute of Technology, Pasadena, CA 91125 

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