Descripción
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Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: ?serving water from a jar? and ?picking up a bottle?. Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application. | |
Internacional
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JCR del ISI
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Si |
Título de la revista
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Expert Systems With Applications |
ISSN
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0957-4174 |
Factor de impacto JCR
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1,924 |
Información de impacto
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Datos JCR del año 2010 |
Volumen
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39 |
DOI
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10.1016/j.eswa.2012.02.143 |
Número de revista
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Desde la página
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9612 |
Hasta la página
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9622 |
Mes
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SIN MES |
Ranking
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