Memorias de investigación
Research Publications in journals:
Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate
Year:2011
Research Areas
  • Medicine,
  • Information technology and adata processing
Information
Abstract
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
International
Si
JCR
Si
Title
Lecture Notes in Artificial Intelligence
ISBN
0302-9743
Impact factor JCR
0,302
Impact info
Datos JCR del año 2005
Volume
Journal number
From page
70
To page
79
Month
SIN MES
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Departamento: Tecnología Fotónica y Bioingeniería