Noncontact Method for Sleep Stage Estimation

Watanabe, Takashi; Watanabe, Kajiro

doi:10.1109/tbme.2004.828037

Cited by 126 publications

(66 citation statements)

References 9 publications

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“…Previous studies on the estimation of sleep condition using heart rate reported approximately 83% NonREM consistency [18], and 92% SWS consistency [19]. It is difficult to compare the estimation accuracy between our method and previous studies using heart rate (HR), since these earlier reports tested adults.…”

Section: Evaluation Methods Of the Estimated Resultsmentioning

confidence: 79%

Novel Sleep Stage Estimation Method for Children Using Body Movement

Okada¹,

Shimizu²,

Ohno³

et al. 2013

PHF

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Abstract-Sleep dysfunction among children is the subject of increasing research attention. However, current methods for assessing daily childhood sleep are problematic, and there are no techniques for estimating sleep stage from body movement alone. Here we propose a new method for estimating sleep stages in children using video analysis of body movement. We examined eight healthy children using polysomnography (

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Section: Evaluation Methods Of the Estimated Resultsmentioning

confidence: 79%

Novel Sleep Stage Estimation Method for Children Using Body Movement

Okada¹,

Shimizu²,

Ohno³

et al. 2013

PHF

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show abstract

“…Information about environmental conditions is relevant in all types of exercises and stages of the therapy. Biosignals such as heart rate variability, EEG, muscle tension, skin conductance, movement, and respiration give important information about the degree of relaxation, and about sleep and sleep stages [44,46]. In the future, automated snoring detection may be of great value in the alignment phase.…”

Section: Ubiquity and Mobile Devices In Cbt-imentioning

confidence: 99%

Persuasive strategies in mobile insomnia therapy: alignment, adaptation, and motivational support

Beun

2012

Pers Ubiquit Comput

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The aim of this paper is to reflect on the underlying principles of insomnia therapy in relation to the use of mobile technology and to present a starting point in the design of computerized insomnia treatment. Three general persuasive strategies are presented for the design of a virtual mobile coach that functions as a first intervention for insomnia treatment in a stepped care context. The presented strategies are based on general principles and methods from cognitive behavior therapy and communication theory. In this paper, it is assumed that the motivation to perform the therapy exercises significantly can be improved by individual tailoring to the characteristics and the circumstances of the user. Two of the presented strategies aim at tailoring the intervention: alignment and adaptation; the third strategy, motivational support, aims at the coach's communicative activities to take away motivational blockades. Given the characteristics of the insomnia treatment, it is concluded that mobile and ubiquitous systems are promising monitoring and delivery devices in a first intervention, provided that the information is reliable and that obtrusiveness is minimized.

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“…REM sleep / NREM sleep) cannot be determined from the data. More recently, researchers have focused on measuring heart/pulse rate and analyzing its variability: HRV (Watanabe, 2004, Michimori, 2003and Wakuda, 2007. The sleep stages can be calculated from HRV if the indices of HRV are properly mapped for the sleep stages.…”

Section: Related Workmentioning

confidence: 99%

Development of a Sleep Monitoring System With Wearable Vital Sensor for Home Use

Suzuki¹,

Ouchi²,

Kameyama³

et al. 2009

Proceedings of the International Conference on Biomedical Electronics and Devices

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Abstract:This paper describes a new sleep monitoring system for home use. The basic system consists of a wearable physiological sensor and PC software for analyzing sleep quality from user's wrist motion and heart rate variability. Different from a conventional sleep monitoring device used in a hospital, the sensor is so small and easy-to-use that a normal person can use it at home. This means that the system is useful for a sleep specialist who wants to check a patient's daily sleep pattern. The system can also be used for self-care. We have developed a wrist-watch-shaped physiological sensor that monitors user's wrist motion and pulse wave interval. We have also developed the algorithm for computing the quality of sleep from these physiological data on PC. Although sleep is a kind of brain activity and our sensor can not directly measure it, the output of our algorithm is close to medically evaluated sleep quality. We performed dozens of comparison experiments and found that its accuracy was about 73.5% on average. The value of the accuracy is enough for assessing a normal person's sleep quality.

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Noncontact Method for Sleep Stage Estimation

Cited by 126 publications

References 9 publications

Novel Sleep Stage Estimation Method for Children Using Body Movement

Novel Sleep Stage Estimation Method for Children Using Body Movement

Persuasive strategies in mobile insomnia therapy: alignment, adaptation, and motivational support

Development of a Sleep Monitoring System With Wearable Vital Sensor for Home Use

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