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Development of a non-contact sleep monitoring system for children
Daily monitoring is important, even for healthy children, because sleep plays a critical role in their development and growth. Polysomnography is necessary for sleep monitoring. However, measuring sleep requires specialized equipment and knowledge and is difficult to do at home. In recent years, sma...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393414/ https://www.ncbi.nlm.nih.gov/pubmed/36003190 http://dx.doi.org/10.3389/fdgth.2022.877234 |
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author | Kamon, Masamitsu Okada, Shima Furuta, Masafumi Yoshida, Koki |
author_facet | Kamon, Masamitsu Okada, Shima Furuta, Masafumi Yoshida, Koki |
author_sort | Kamon, Masamitsu |
collection | PubMed |
description | Daily monitoring is important, even for healthy children, because sleep plays a critical role in their development and growth. Polysomnography is necessary for sleep monitoring. However, measuring sleep requires specialized equipment and knowledge and is difficult to do at home. In recent years, smartwatches and other devices have been developed to easily measure sleep. However, they cannot measure children's sleep, and contact devices may disturb their sleep. A non-contact method of measuring sleep is the use of video during sleep. This is most suitable for the daily monitoring of children’s sleep, as it is simple and inexpensive. However, the algorithms have been developed only based on adult sleep, whereas children’s sleep is known to differ considerably from that of adults. For this reason, we conducted a non-contact estimation of sleep stages for children using video. The participants were children between the ages of 0–6 years old. We estimated the four stages of sleep using the body movement information calculated from the videos recorded. Six parameters were calculated from body movement information. As children’s sleep is known to change significantly as they grow, estimation was divided into two groups (0–2 and 3–6 years). The results show average estimation accuracies of 46.7 ± 6.6 and 49.0 ± 4.8% and kappa coefficients of 0.24 ± 0.11 and 0.28 ± 0.06 in the age groups of 0–2 and 3–6 years, respectively. This performance is comparable to or better than that reported in previous adult studies. |
format | Online Article Text |
id | pubmed-9393414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93934142022-08-23 Development of a non-contact sleep monitoring system for children Kamon, Masamitsu Okada, Shima Furuta, Masafumi Yoshida, Koki Front Digit Health Digital Health Daily monitoring is important, even for healthy children, because sleep plays a critical role in their development and growth. Polysomnography is necessary for sleep monitoring. However, measuring sleep requires specialized equipment and knowledge and is difficult to do at home. In recent years, smartwatches and other devices have been developed to easily measure sleep. However, they cannot measure children's sleep, and contact devices may disturb their sleep. A non-contact method of measuring sleep is the use of video during sleep. This is most suitable for the daily monitoring of children’s sleep, as it is simple and inexpensive. However, the algorithms have been developed only based on adult sleep, whereas children’s sleep is known to differ considerably from that of adults. For this reason, we conducted a non-contact estimation of sleep stages for children using video. The participants were children between the ages of 0–6 years old. We estimated the four stages of sleep using the body movement information calculated from the videos recorded. Six parameters were calculated from body movement information. As children’s sleep is known to change significantly as they grow, estimation was divided into two groups (0–2 and 3–6 years). The results show average estimation accuracies of 46.7 ± 6.6 and 49.0 ± 4.8% and kappa coefficients of 0.24 ± 0.11 and 0.28 ± 0.06 in the age groups of 0–2 and 3–6 years, respectively. This performance is comparable to or better than that reported in previous adult studies. Frontiers Media S.A. 2022-08-08 /pmc/articles/PMC9393414/ /pubmed/36003190 http://dx.doi.org/10.3389/fdgth.2022.877234 Text en © 2022 Kamon, Okada, Furuta and Yosida. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Kamon, Masamitsu Okada, Shima Furuta, Masafumi Yoshida, Koki Development of a non-contact sleep monitoring system for children |
title | Development of a non-contact sleep monitoring system for children |
title_full | Development of a non-contact sleep monitoring system for children |
title_fullStr | Development of a non-contact sleep monitoring system for children |
title_full_unstemmed | Development of a non-contact sleep monitoring system for children |
title_short | Development of a non-contact sleep monitoring system for children |
title_sort | development of a non-contact sleep monitoring system for children |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393414/ https://www.ncbi.nlm.nih.gov/pubmed/36003190 http://dx.doi.org/10.3389/fdgth.2022.877234 |
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