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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.877234/full |
| _version_ | 1828428236871172096 |
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| author | Masamitsu Kamon Shima Okada Masafumi Furuta Koki Yoshida |
| author_facet | Masamitsu Kamon Shima Okada Masafumi Furuta Koki Yoshida |
| author_sort | Masamitsu Kamon |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T17:14:23Z |
| format | Article |
| id | doaj.art-db813b8d026f40edb47f07d19a39488d |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-12-10T17:14:23Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-db813b8d026f40edb47f07d19a39488d2022-12-22T01:40:11ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-08-01410.3389/fdgth.2022.877234877234Development of a non-contact sleep monitoring system for childrenMasamitsu Kamon0Shima Okada1Masafumi Furuta2Koki Yoshida3Department of Robotics, Ritsumeikan University, Shiga, JapanDepartment of Robotics, Ritsumeikan University, Shiga, JapanTechnology Research Laboratory, Shimadzu Corporation, Kyoto, JapanTechnology Research Laboratory, Shimadzu Corporation, Kyoto, JapanDaily 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.https://www.frontiersin.org/articles/10.3389/fdgth.2022.877234/fullsleep stagesleep monitoringchildrenvideo monitoringvideo image processingmachine leaning |
| spellingShingle | Masamitsu Kamon Shima Okada Masafumi Furuta Koki Yoshida Development of a non-contact sleep monitoring system for children Frontiers in Digital Health sleep stage sleep monitoring children video monitoring video image processing machine leaning |
| 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 | sleep stage sleep monitoring children video monitoring video image processing machine leaning |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.877234/full |
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