RT Journal Article
T1 Assessing Physical Activity and Functional Fitness Level Using Convolutional Neural Networks.
A1 Galán Mercant, Alejandro
A1 Ortiz-García, Andrés
A1 Herrera Viedma, Enrique
A1 Tomas, Maria Teresa
A1 Fernandes, Beatriz
A1 Moral-Muñoz, Jose A.
K1 Redes neuronales (Informática)
K1 Ejercicio físico - Evaluación
K1 Ancianos - Condición física
AB 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.
PB Elsevier
YR 2019
FD 2019-12-01
LK https://hdl.handle.net/10630/28103
UL https://hdl.handle.net/10630/28103
LA eng
NO Galán-Mercant, Alejandro & Ortiz, Andrés & Herrera-Viedma, Enrique & Tomás, Maria Teresa & Fernandes, Beatriz & Moral-Munoz, Jose. (2019). Assessing Physical Activity and Functional Fitness Level Using Convolutional Neural Networks. Knowledge-Based Systems. 185. 10.1016/j.knosys.2019.104939.
NO This work was partly supported by the MINECO/FEDER under TEC2015-64718-R, PSI2015-65848-R and PGC2018-098813-B-C32 projects; and the Erasmus+ Strategic Partnership for Higher Education Programme (Key Action 203) [Grant number: 2018-1-PL01-KA203-051055]. Furthermore, the mobility grant EST2018-090 was supported by the University of Cádiz . We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026