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Języki publikacji
Abstrakty
This paper presents the use of an artificial neural network (NN) approach for predicting the muscle forces around the elbow joint. The main goal was to create an artificial NN which could predict the musculotendon forces for any general muscle without significant errors. The input parameters for the network were morphological and anatomical musculotendon parameters, plus an activation level experimentally measured during a flexion/extension movement in the elbow. The muscle forces calculated by the ‘Virtual Muscle System’ provide the output. The cross-correlation coefficient expressing the ability of an artificial NN to predict the “true” force was in the range 0.97 - 0.98. A sensitivity analysis was used to eliminate the less sensitive inputs, and the final number of inputs for a sufficient prediction was nine. A variant of an artificial NN for a single specific muscle was also studied. The artificial NN for one specific muscle gives better results than a network for general muscles. This method is a good alternative to other approaches to calculating of muscle force.
Czasopismo
Rocznik
Tom
Strony
119--127
Opis fizyczny
Bibliogr. 39 poz. rys., tab., wykr.
Twórcy
autor
- Faculty of Mechanical Engineering, Czech Technical University in Prague
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-99f60e12-192e-4a77-9f4e-200861e701bc