A MINIMAL CONTROL SCHEMA FOR GOAL-DIRECTED ARM MOVEMENTS BASED ON PHYSIOLOGICAL INTER-JOINT COUPLINGS

Till Bockemühl, Volker Dürr

2010

Abstract

Substantial evidence suggests that nervous systems simplify motor control of complex body geometries by use of higher level functional units, so called motor primitives or synergies. Although simpler, such high level functional units still require an adequate controller. In a previous study, we found that kinematic inter-joint couplings allow the extraction of simple movement synergies during unconstrained 3D catching movements of the human arm and shoulder girdle. Here, we show that there is a bijective mapping between movement synergy space and 3D Cartesian hand coordinates within the arm’s physiological working range. Based on this mapping, we propose a minimal control schema for a 10-DoF arm and shoulder girdle. All key elements of this schema are implemented as artificial neural networks (ANNs). For the central controller, we evaluate two different ANN architectures: a feed-forward network and a recurrent Elman network. We show that this control schema is capable of controlling goal-directed movements of a 10-DoF arm with as few as five hidden units. Both controller variants are sufficient for the task. However, end-point stability is better in the feed-forward controller.

References

  1. Atkeson, C. G., Hollerbach, J. M. (1985). Kinematic features of unrestrained vertical arm movements. The Journal of Neuroscience 5, 2318 - 2330.
  2. Bernstein, N. (1967). The co-ordination and regulation of movements. New York: Pergamon Press Ltd.
  3. Bockemühl, T., Troje, N. F., and Dürr, V. (2010). Interjoint coupling and joint angle synergies of human catching movements. Human Movement Science 29, 73 - 93.
  4. Choi, K., Hirose H., Sakurai, Y., Iijima, T., Koike, Y. (2009). Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture. Neural Networks 22, 1214 - 1223.
  5. d'Avella, A., Portone, A., Fernandez, L., Lacquaniti, F. (2006). Control of Fast-Reaching Movements by Muscle Synergy Combinations. The Journal of Neuroscience 26, 7791 - 7810.
  6. Elman, J. L. (1990). Finding Structure in Time. Cognitive Science 14, 179 - 211.
  7. Flash, T., Hochner, B. (2005). Motor primitives in vertebrates and invertebrates. Current Opinion in Neurobiology 15, 660 - 666.
  8. Flash, T., Hogan, N. (1985). The coordination of arm movements: An experimentally confirmed mathematical model. The Journal of Neuroscience, 5, 1688 - 1703.
  9. Karniel A., Inbar G. (1997). A model for learning human reaching movements. Biological Cybernetics 77, 173 - 183.
  10. Kawato, M., Maeda Y., Uno, Y., Suzuki R. (1990). Trajectory formation of arm movement by cascade neural network model based on minimum torquechange criterion. Biological Cybernetics 62, 275 - 288.
  11. Koike, Y., Hirose, H., Sakurai, Y., Iijima, T. (2006). Prediction of arm trajectory from a small number of neuron activities in the primary motor cortex. Neuroscience Research 55, 146 - 153.
  12. Levenberg, K. (1944). A method for the solution of certain nonlinear problems in least squares. Quarterly Journal of Applied Mathematics 2, 164 - 168.
  13. Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Siam Journal on Applied Mathematics 11, 431 - 441.
  14. Massone, L., Bizzi, E. (1989). A neural network model for limb trajectory formation. Biological Cybernetics 61, 417 - 425.
  15. Massone, L., Myers, J. (1994). A Neural Network Model of an Anthropomorphic Arm. IEEE Transactions on Systems, Man, and Cybernetics [B] 26, 719 - 732.
  16. Mussa-Ivaldi, F. A., Giszter, S. F., Bizzi, E. (1994). Linear combinations of primitives in vertebrate motor control. Proceedings of the National Academy of Sciences of the United States of America 91, 7534 - 7538.
  17. Ting, L. H., McKay, J. L. (2007). Neuromechanics of muscle synergies for posture and movement. Current Opinion in Neurobiology. 17, 622 - 628.
  18. Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience 7, 907 - 915.
  19. Uno, Y., Kawato M., Suzuki R. (1989). Formation and control of optimal trajectory in human multijoint arm movement. Biological Cybernetics 61, 89 - 101.
  20. Wolpert, M., Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience 3, 1212-1217.
Download


Paper Citation


in Harvard Style

Bockemühl T. and Dürr V. (2010). A MINIMAL CONTROL SCHEMA FOR GOAL-DIRECTED ARM MOVEMENTS BASED ON PHYSIOLOGICAL INTER-JOINT COUPLINGS . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 220-226. DOI: 10.5220/0003084102200226


in Bibtex Style

@conference{icnc10,
author={Till Bockemühl and Volker Dürr},
title={A MINIMAL CONTROL SCHEMA FOR GOAL-DIRECTED ARM MOVEMENTS BASED ON PHYSIOLOGICAL INTER-JOINT COUPLINGS},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={220-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003084102200226},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - A MINIMAL CONTROL SCHEMA FOR GOAL-DIRECTED ARM MOVEMENTS BASED ON PHYSIOLOGICAL INTER-JOINT COUPLINGS
SN - 978-989-8425-32-4
AU - Bockemühl T.
AU - Dürr V.
PY - 2010
SP - 220
EP - 226
DO - 10.5220/0003084102200226