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Machine Learning for Prediction of Muscle Activations for a Rule-Based Controller

S. Jonić, D. Popović

Proceedings of the Nineteenth Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'97), Chicago IL, USA, October 30-November 2, 1997, vol. 4, pp. 1781-1784.


The inductive learning (IL) technique, radial basis function (RBF) type of artificial neural network (ANN), and the combination of IL and RBF were used to predict muscle activation patterns and sensory data based on the preceding sensory data. The input consisted of the hip and knee joint angles, horizontal and vertical ground reaction forces recorded in an able-bodied human. The output data consisted of the patterns of muscle activities. These patterns were obtained from simulation of walking with a functional electrical stimulation (FES) system. The simulation takes into account the individual biomechanical characteristics of the eventual user having spinal cord injury (SCI). The mappings were tested using numerous data from five minutes of walking previously not used for the training. We illustrate the technique by presenting the estimation of the activations of the equivalent flexor knee muscle and the knee joint sensor for four strides. The correlation is better and tracking errors are smaller when the combination of IL and RBF is used compared to the usage of IL or RBF. We show that the prediction of sensory state is achievable; thus, the delays imposed by the properties of the neuro-muscular system can be minimized.

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AUTHOR="Joni{\'{c}}, S. and Popovi{\'{c}}, D.",
TITLE="Machine Learning for Prediction of Muscle Activations for a
	Rule-Based Controller",
BOOKTITLE="Proceedings of the Nineteenth Annual International
	Conference of the {IEEE} Engineering in Medicine and Biology Society
	({EMBS'97})",
YEAR="1997",
editor="",
volume="4",
series="",
pages="1781--1784",
address="Chicago IL, USA",
month="October 30-November 2,",
organization="",
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© 1997 EMBS. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from EMBS. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
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