Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data Articles uri icon

authors

  • GARCIA GARCIA, FERNANDO
  • BENITO, PEDRO J.
  • HERNANDO, MARIA E.

publication date

  • January 2016

start page

  • 533

end page

  • 544

issue

  • 06

volume

  • 55

International Standard Serial Number (ISSN)

  • 00261270 (ISSN)

abstract

  • Background: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling.

    Methods: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low / moderate / vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic / resistance / mixed). In to -tal, 178.63 h of data about PA intensity (65.55 % low / 18.96 % moderate / 15.49 % vigorous) and 17.00 h about modality were collected in two experiments: one in free- living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall.

    Results: The best scheme, which comprised a projection through Linear Discriminant Ana -lysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65 %, versus up to 63.60 %. Errors tended to be brief and to appear around transients.

    Conclusions: The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation.

subjects

  • Biology and Biomedicine
  • Medicine

keywords

  • physical activity intensity; exercise modality; accelerometer; heart rate; clustering