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Determining Synergy Between Joint Angles During Locomotion by Radial Basis Function Neural Networks

D. Popović, S. Jonić

Proceedings of the Twentieth Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'98), Hong Kong, People's Republic of China, October 29-November 1, 1998, vol. 20, no. 5, pp. 2301-2304.



This paper shows that the radial basis function neural networks are suitable tools for determining the synergies between the leg joint angles for cyclic activities. The study was motivated by earlier studies showing the following: 1) cyclic functional movements (e.g., walking) are synergistic [1]; and 2) machine learning techniques for recognizing gait events perform similar when a training set includes one or more joint angles [2]. The results of this study prove that the only one joint angle sensor is sufficient to describe a cyclic motor pattern, and that the second joint angle sensor is redundant for cyclic activities, but very useful to detect the change of the mode of locomotion or hazard [3]. The results of the study will be implemented for restoring walking of humans with disabilities using a functional electrical stimulation system.

References

  1. N.A. Bernstein, The co-ordination and regulation of movements, Pergamon Press, Oxford, 1967.

  2. S. Jonić, T. Janković, V. Gajić, D. Popović, "Three Machine Learning Techniques for Automatic Determination of Rules to Control Locomotion," IEEE Transactions on Biomedical Engineering, vol. 46, no. 3, pp. 300-310, March 1999.

  3. D. Popović, R. Tomović, D. Tepavac, L. Schwirtlich, "Control Aspects of Active Above-Knee Prosthesis," International Journal of Man-Machine Studies, vol. 35, no. 6, pp. 751-767, December 1991.


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