Neural Networks for Muscle Forces Prediction in Cycling

Algorithms

Giulio Cecchini²  Gabriele Maria Lozito²  Maurizio Schmid²  Silvia Conforto²  Francesco Riganti Fulginei²  Daniele Bibbo¹ 

[1] Department of Engineering, Roma Tre University, Via Vito Volterra 62-Corpo B, Rome 00146, Italy;

关键词: Artificial Neural Networks;    muscle forces;    cycling;    inverse dynamics;    inverse problems solution.;   

DOI  :  10.3390/a7040621

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.