Assessing physical activity and functional fitness level using convolutional neural networks

Older adults are related to a reduction in physical functionality, as a result of a musculoskeletal system degeneration. In that way, physical exercise has been stated as a suitable intervention to prevent such health problems. Therefore, an adequate assessment of the physical activity and functiona...

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Podrobná bibliografia
Vydané v:	Knowledge-based systems Ročník 185; s. 104939
Hlavní autori:	Galán-Mercant, Alejandro, Ortiz, Andrés, Herrera-Viedma, Enrique, Tomas, Maria Teresa, Fernandes, Beatriz, Moral-Munoz, Jose A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.12.2019 Elsevier Science Ltd
Predmet:	Acceleration Accelerometers Adults Anthropometry Artificial neural networks Body height Body mass index Body size Body weight Deep Convolutional Autoencoder/sep Convolutional Network Deep learning Degeneration Exercise Functional fitness Health problems Health services Inertial signal Intervention Learning Machine learning Musculoskeletal system Neural networks Older people Physical ability Physical activity Physical exercise Physical fitness Deep learning Inertial signal Deep Convolutional Autoencoder/sep Convolutional Network Physical activity Functional fitness
ISSN:	0950-7051, 1872-7409
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Popis
Shrnutí:	Older adults are related to a reduction in physical functionality, as a result of a musculoskeletal system degeneration. In that way, physical exercise has been stated as a suitable intervention to prevent such health problems. Therefore, an adequate assessment of the physical activity and functional fitness levels is needed to plan the individualized intervention. A broad test used to assess the functional fitness level is the 6-minutes walk test (6MWT). It has been previously measured using accelerometer sensors. In views of this background, the main aim of the present study is to use deep learning to extract automatically and to predict the physical activity and functional fitness levels of the older adults through the acceleration signals recorded by a smartphone during the 6MWT. A total of 17 participants were recruited. Anthropometric measurements (weight, height, and body mass index), physical activity, and functional fitness levels from each participant were recorded. Consecutively, two deep learning-based methods were applied to determine the prediction. According to the results, the proposed method can predict physical activity and functional fitness levels with high accuracy, even using only one cycle. Thus, the approach described in the present work could be implemented in future mobile health systems to identify the physical activity profile of older adults.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0950-7051 1872-7409
DOI:	10.1016/j.knosys.2019.104939