title: Automatic Identification of Physical Activity Intensity and Modality from the Fusion of Accelerometry and Heart Rate Data.
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 total, 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 Analysis (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.
authors: Fernando García-García, ETSI Telecomunicación (B-303), Avda. Complutense 30, 28040, Madrid, Spain, E-mail: fgarcia@gbt.tfo.upm.es.
authors: Fernando
authors: F
authors: García-García
authors: Pedro J
authors: PJ
authors: Benito
authors: María E
authors: ME
authors: Hernando
mesh_terms: D061725:Accelerometry; D000328:Adult; D000465:Algorithms; D001331:Automation; D019992:Databases as Topic; D015444:Exercise; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted
keywords: Physical activity intensity; accelerometer; clustering; exercise modality; heart rate