ArticleFrontiers in digital health2022
Development of a non-contact sleep monitoring system for children.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Noncontact respiratory monitoring during sleep: comparison of the touchless flow signal with respiratory inductance plethysmography flow (RIPJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine.Journal of personalized medicine · 2024Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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