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

García García, Fernando; Benito Peinado, Pedro José y Hernando Pérez, Maria Elena (2016). Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data. "Methods of Information in Medicine", v. 55 (n. 6); pp. 533-544. ISSN 00261270. https://doi.org/10.3414/ME15-01-0130.

Descripción

Título: Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data
Autor/es:
  • García García, Fernando
  • Benito Peinado, Pedro José
  • Hernando Pérez, Maria Elena
Tipo de Documento: Artículo
Título de Revista/Publicación: Methods of Information in Medicine
Fecha: 2016
Volumen: 55
Materias:
Palabras Clave Informales: Clustering, heart rate, accelerometer, Physical activity intensity, exercise modality
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica y Bioingeniería
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Universidad Politécnica de MadridFIS PS09/01318APRIORISin especificarSin especificar
Gobierno de EspañaDEP-2008-06354-C04-01PRONAFSin especificarSin especificar

Más información

ID de Registro: 46038
Identificador DC: http://oa.upm.es/46038/
Identificador OAI: oai:oa.upm.es:46038
Identificador DOI: 10.3414/ME15-01-0130
URL Oficial: https://methods.schattauer.de/en/contents/archivestandard/issue/2436/manuscript/26264.html
Depositado por: Memoria Investigacion
Depositado el: 10 Jul 2017 15:56
Ultima Modificación: 10 Jul 2017 15:56
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