Feasibility of estimating isokinetic knee torque using a neural network model.

Feasibility of estimating isokinetic knee torque using a neural network model. Research Abstract Details 

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Feasibility of estimating isokinetic knee torque using a neural network model. Abstract Text:

Michael E Hahn,

Many studies have investigated the relationships between electromyography (EMG) and torque production. A few investigators have used adjusted learning algorithms and feed-forward artificial neural networks (ANNs) to estimate joint torque in the elbow. This study sought to estimate net isokinetic knee torque using ANN models. Isokinetic knee extensor and flexor torque data were measured simultaneously with agonist and antagonist EMG during concentric and eccentric contractions at joint velocities of 30 degrees /s and 60 degrees /s. Age, gender, height, body mass, agonist EMG, antagonist EMG, joint position and joint velocity were entered as predictive variables of net torque. A three-layer ANN model was developed and trained using an adjusted back-propagation algorithm. Accuracy results were compared against those of forward stepwise regression models. Stepwise regression models included body mass, body height and joint position as the most influential predictors, followed by agonist EMG for concentric and eccentric contractions. Estimation of eccentric torque included antagonist EMG following the agonist activation. ANN models resulted in more accurate torque estimation (R=0.96), compared to the stepwise regression models (R=0.71). ANN model accuracy increased greatly when the number of hidden units increased from 5 to 10, continuing to increase gradually with additional hidden units. The average number of training epochs necessary for solution convergence and the relative accuracy of the model indicate a strong ability for the ANN model to generalize these estimations to a broader sample. The ANN model appears to be a feasible technique for estimating joint torque in the knee.

Feasibility of estimating isokinetic knee torque using a neural network model. Publishing Authors By Initials

ME Hahn,

For similar biomechanics: torsion, mechanical: torque research abstracts see: biomechanics: torsion, mechanical: torque research

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Feasibility of estimating isokinetic knee torque using a neural network model. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Journal of biomechanics

VOLUME: 40

Page Numbers: 1107-14

Journal Abbreviation:

ISSN: 0021-9290

DAY: 15

MONTH: 06

YEAR: 2006

Feasibility of estimating isokinetic knee torque using a neural network model. Information

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LANGUAGE: eng

NlmUniqueID: 157375

Feasibility of estimating isokinetic knee torque using a neural network model. Keywords Mesh Terms:

KEYWORDS: Torque

MESH TERMS: physiology

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Grant and Affiliation Information for Feasibility of estimating isokinetic knee torque using a neural network model.

AFFILIATION: Department of Health and Human Development, Montana State University, P.O. Box 173360, Bozeman, MT 59717, USA. mhahn@montana.edu

Country: United States

AGENCY: United States NCRR

GRANT: P20 RR-16455-01

ACRONYM: RR

MEDLINETA: J Biomech

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