Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

Garcia Garcia, Fernando; García Sáez, Gema; Chausa Fernández, Paloma; Martínez Sarriegui, Iñaki; Benito Peinado, Pedro José; Gómez Aguilera, Enrique J. y Hernando Pérez, María Elena (2011). Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate. En: "AIME'11 13th conference on Artificial intelligence in medicine", 02/07/2011 - 06/072011, Bled, Eslovenia. ISBN 978-3-642-22217-7.

Descripción

Título: Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate
Autor/es:
  • Garcia Garcia, Fernando
  • García Sáez, Gema
  • Chausa Fernández, Paloma
  • Martínez Sarriegui, Iñaki
  • Benito Peinado, Pedro José
  • Gómez Aguilera, Enrique J.
  • Hernando Pérez, María Elena
Tipo de Documento: Ponencia en Congreso o Jornada (Sin especificar)
Título del Evento: AIME'11 13th conference on Artificial intelligence in medicine
Fechas del Evento: 02/07/2011 - 06/072011
Lugar del Evento: Bled, Eslovenia
Título del Libro: AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Fecha: 2011
ISBN: 978-3-642-22217-7
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

Más información

ID de Registro: 14157
Identificador DC: http://oa.upm.es/14157/
Identificador OAI: oai:oa.upm.es:14157
URL Oficial: http://www.aimedicine.info/aime11/
Depositado por: Memoria Investigacion
Depositado el: 19 Dic 2012 09:55
Ultima Modificación: 21 Abr 2016 13:41
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