Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Fernando García-García , Gema García-Sáez , Paloma Chausa , Iñaki Martínez-Sarriegui , Pedro José Benito , Enrique J. Gómez , M. Elena Hernando

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. More... »

PAGES

70-79

References to SciGraph publications

  • 2006-12. Estimating energy expenditure using accelerometers in EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY
  • 2006. A Practical Approach to Recognizing Physical Activities in PERVASIVE COMPUTING
  • 2004. Activity Recognition from User-Annotated Acceleration Data in PERVASIVE COMPUTING
  • Book

    TITLE

    Artificial Intelligence in Medicine

    ISBN

    978-3-642-22217-7
    978-3-642-22218-4

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-22218-4_9

    DOI

    http://dx.doi.org/10.1007/978-3-642-22218-4_9

    DIMENSIONS

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