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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| Vydané v: | Knowledge-based systems Ročník 185; s. 104939 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
01.12.2019
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 104939 |
| Author | Ortiz, Andrés Galán-Mercant, Alejandro Herrera-Viedma, Enrique Fernandes, Beatriz Moral-Munoz, Jose A. Tomas, Maria Teresa |
| Author_xml | – sequence: 1 givenname: Alejandro surname: Galán-Mercant fullname: Galán-Mercant, Alejandro email: alejandro.galan@uca.es organization: Department of Nursing and Physiotherapy, Universidad de Cádiz, Cádiz, Spain – sequence: 2 givenname: Andrés surname: Ortiz fullname: Ortiz, Andrés email: aortiz@ic.uma.es organization: Communications Engineering Department, University of Málaga, Málaga, Spain – sequence: 3 givenname: Enrique surname: Herrera-Viedma fullname: Herrera-Viedma, Enrique email: viedma@decsai.ugr.es organization: Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), Spain – sequence: 4 givenname: Maria Teresa surname: Tomas fullname: Tomas, Maria Teresa email: teresa.tomas@estesl.ipl.pt organization: Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Instituto Politécnico de Lisboa, Lisboa, Portugal – sequence: 5 givenname: Beatriz surname: Fernandes fullname: Fernandes, Beatriz email: beatriz.fernandes@estesl.ipl.pt organization: Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Instituto Politécnico de Lisboa, Lisboa, Portugal – sequence: 6 givenname: Jose A. surname: Moral-Munoz fullname: Moral-Munoz, Jose A. email: joseantonio.moral@uca.es organization: Department of Nursing and Physiotherapy, Universidad de Cádiz, Cádiz, Spain |
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| Keywords | Deep learning Inertial signal Deep Convolutional Autoencoder/sep Convolutional Network Physical activity Functional fitness |
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| Title | Assessing physical activity and functional fitness level using convolutional neural networks |
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