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...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge-based systems Vol. 185; p. 104939
Main Authors: Galán-Mercant, Alejandro, Ortiz, Andrés, Herrera-Viedma, Enrique, Tomas, Maria Teresa, Fernandes, Beatriz, Moral-Munoz, Jose A.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2019
Elsevier Science Ltd
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography: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