The variance needed to accurately describe jump height from vertical ground reaction force data.
J Appl Biomech
; 30(6): 732-6, 2014 Dec.
Article
en En
| MEDLINE
| ID: mdl-25010220
ABSTRACT
In functional principal component analysis (fPCA) a threshold is chosen to define the number of retained principal components, which corresponds to the amount of preserved information. A variety of thresholds have been used in previous studies and the chosen threshold is often not evaluated. The aim of this study is to identify the optimal threshold that preserves the information needed to describe a jump height accurately utilizing vertical ground reaction force (vGRF) curves. To find an optimal threshold, a neural network was used to predict jump height from vGRF curve measures generated using different fPCA thresholds. The findings indicate that a threshold from 99% to 99.9% (6-11 principal components) is optimal for describing jump height, as these thresholds generated significantly lower jump height prediction errors than other thresholds.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Análisis y Desempeño de Tareas
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Reconocimiento de Normas Patrones Automatizadas
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Interpretación Estadística de Datos
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Redes Neurales de la Computación
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Análisis de Componente Principal
/
Pie
/
Movimiento
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Adult
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Humans
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Male
Idioma:
En
Revista:
J Appl Biomech
Año:
2014
Tipo del documento:
Article
País de afiliación:
Irlanda
Pais de publicación:
EEUU
/
ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA