Memorias de investigación
Artículos en revistas:
Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate
Año:2011
Áreas de investigación
  • Medicina,
  • Ciencias de la computación y tecnología informática
Datos
Descripción
This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators
Internacional
Si
JCR del ISI
Si
Título de la revista
Lecture Notes in Artificial Intelligence
ISSN
0302-9743
Factor de impacto JCR
0,302
Información de impacto
Datos JCR del año 2005
Volumen
DOI
Número de revista
Desde la página
70
Hasta la página
79
Mes
SIN MES
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Tecnología Fotónica y Bioingeniería