Performance test of multiplatform real time processing of biomedical signals

Krejcar, Ondřej; Penhaker, Marek; Janckulik, Dalibor; Motalova, Leona

doi:10.1109/indin.2010.5549635

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…The function approximation capability allows them to form arbitrary nonlinear discriminant surfaces, while the generalization ability allows responding consistently to data never shown to the network itself. In this work, NNs have been used with the aim of obtaining a good approximation of the estimated muscle forces with a reduced computational cost, that represents one of the main targets for real time application in biomedical application [30,31].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Muscle Forces Prediction in Cycling

et al. 2014

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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.

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Section: Introductionmentioning

confidence: 99%