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Algorithms 2014, 7(4), 621-634; doi:10.3390/a7040621

Neural Networks for Muscle Forces Prediction in Cycling

Department of Engineering, Roma Tre University, Via Vito Volterra 62-Corpo B, Rome 00146, Italy
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Received: 3 September 2014 / Revised: 27 October 2014 / Accepted: 11 November 2014 / Published: 13 November 2014
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Abstract

This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application. View Full-Text
Keywords: Artificial Neural Networks; muscle forces; cycling; inverse dynamics; inverse problems solution. Artificial Neural Networks; muscle forces; cycling; inverse dynamics; inverse problems solution.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cecchini, G.; Lozito, G.M.; Schmid, M.; Conforto, S.; Fulginei, F.R.; Bibbo, D. Neural Networks for Muscle Forces Prediction in Cycling. Algorithms 2014, 7, 621-634.

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