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Synergistic control of forearm based on accelerometer data and artificial neural networks BJMBR
Mijovic,B.; Popovic,M.B.; Popovic,D.B..
In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Synergy; Accelerometers; Artificial neural network; Control; Neural prosthesis; Functional electrical stimulation.
Ano: 2008 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2008000500007
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