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Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472058/ https://www.ncbi.nlm.nih.gov/pubmed/32764316 http://dx.doi.org/10.3390/s20164364 |
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author | Ahmadi, Matthew N. Pavey, Toby G. Trost, Stewart G. |
author_facet | Ahmadi, Matthew N. Pavey, Toby G. Trost, Stewart G. |
author_sort | Ahmadi, Matthew N. |
collection | PubMed |
description | Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions. |
format | Online Article Text |
id | pubmed-7472058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74720582020-09-04 Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children Ahmadi, Matthew N. Pavey, Toby G. Trost, Stewart G. Sensors (Basel) Article Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions. MDPI 2020-08-05 /pmc/articles/PMC7472058/ /pubmed/32764316 http://dx.doi.org/10.3390/s20164364 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ahmadi, Matthew N. Pavey, Toby G. Trost, Stewart G. Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title | Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title_full | Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title_fullStr | Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title_full_unstemmed | Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title_short | Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children |
title_sort | machine learning models for classifying physical activity in free-living preschool children |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472058/ https://www.ncbi.nlm.nih.gov/pubmed/32764316 http://dx.doi.org/10.3390/s20164364 |
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