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...

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Bibliographic Details
Main Authors: Masamitsu Kamon (Author), Shima Okada (Author), Masafumi Furuta (Author), Koki Yoshida (Author)
Format: Book
Published: Frontiers Media S.A., 2022-08-01T00:00:00Z.
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001 doaj_db813b8d026f40edb47f07d19a39488d
042 |a dc 
100 1 0 |a Masamitsu Kamon  |e author 
700 1 0 |a Shima Okada  |e author 
700 1 0 |a Masafumi Furuta  |e author 
700 1 0 |a Koki Yoshida  |e author 
245 0 0 |a Development of a non-contact sleep monitoring system for children 
260 |b Frontiers Media S.A.,   |c 2022-08-01T00:00:00Z. 
500 |a 2673-253X 
500 |a 10.3389/fdgth.2022.877234 
520 |a 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. 
546 |a EN 
690 |a sleep stage 
690 |a sleep monitoring 
690 |a children 
690 |a video monitoring 
690 |a video image processing 
690 |a machine leaning 
690 |a Medicine 
690 |a R 
690 |a Public aspects of medicine 
690 |a RA1-1270 
690 |a Electronic computers. Computer science 
690 |a QA75.5-76.95 
655 7 |a article  |2 local 
786 0 |n Frontiers in Digital Health, Vol 4 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fdgth.2022.877234/full 
787 0 |n https://doaj.org/toc/2673-253X 
856 4 1 |u https://doaj.org/article/db813b8d026f40edb47f07d19a39488d  |z Connect to this object online.