Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data
Año:2016
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Methods of Information in Medicine
ISSN
00261270
Factor de impacto JCR
2,248
Información de impacto
Volumen
55
DOI
10.3414/ME15-01-0130
Número de revista
6
Desde la página
533
Hasta la página
544
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Maria Elena Hernando Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Departamento: Tecnología Fotónica y Bioingeniería
S2i 2022 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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