Memorias de investigación
Research Publications in journals:
Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data
Year:2016

Research Areas
  • Electronic technology and of the communications

Information
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.
International
Si
JCR
Si
Title
Methods of Information in Medicine
ISBN
00261270
Impact factor JCR
2,248
Impact info
Volume
55
10.3414/ME15-01-0130
Journal number
6
From page
533
To page
544
Month
SIN MES
Ranking
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Departamento: Tecnología Fotónica y Bioingeniería