Smart technologies toward sleep monitoring at home

Park, Kwang Suk; Choi, Sang Ho

doi:10.1007/s13534-018-0091-2

Cited by 74 publications

(34 citation statements)

References 72 publications

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“…Sleep efficiency (SE) could be calculated using the formula,

, based on the estimation of the sleep and wake states. In addition, sleep onset latency (SOL) which is time taken to sleep, wake after sleep onset (WASO) which is total waking time after sleep, and total sleep time (TST) which is total sleep time during time in bed could also be obtained from the actigraphy information [ 70 ].…”

Section: Methodsmentioning

confidence: 99%

Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

Shin

Kim

et al. 2020

Sensors

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Several studies, wherein the structure or rigidity of a mattress was varied, have been conducted to improve sleep quality. These studies investigated the effect of variation in the surface characteristics of mattresses on sleep quality. The present study developed a mattress whose rigidity can be varied by controlling the amount of air in its air cells. To investigate the effect of the variable rigidity of the air mattress on sleep quality, participants (Male, Age: 23.9 ± 2.74, BMI: 23.3 ± 1.60) were instructed to sleep on the air mattress under different conditions, and their sleep quality was subjectively and objectively investigated. Subjectively, sleep quality is assessed based on the participants’ evaluations of the depth and length of their sleep. Objectively, sleep is estimated using the sleep stage information obtained by analysing the movements and brain waves of the participants during their sleep. A subjective assessment of the sleep quality demonstrates that the participants’ sleep was worse with the adjustment of the air mattress than that without; however, the objective sleep quality results demonstrates an improvement in the sleep quality when the rigidity of the air mattress is varied based on the participant’s preference. This paper proposes a design for mattresses that can result in more efficient sleep than that provided by traditional mattresses.

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“…Sleep efficiency (SE) could be calculated using the formula,

Section: Methodsmentioning

confidence: 99%

Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

Shin

Kim

et al. 2020

Sensors

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“…• Actigraphy: This is a non-invasive, wrist-worn device that comes with accelerometer to measure sleep pattern by distinguishing between the states of wakefulness and sleep unobtrusively. This is based on the premise that limited movement is associated with sleep while increased movement is linked with wakefulness [22,23].…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

“…The unnatural setting and the need for a sleep technologist to set up PSG equipment make its use impractical in a home setting. Consumer wearable sleep monitoring sensors, called actigraphy, is a non-invasive wrist-worn device that comes with accelerometer, heart rate, and respiratory monitor to detect and record the movements of the wearer at regular intervals in order to estimate sleep and wakefulness [13,22,23,30,31].…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

“…Actigraphy has been shown to be accurate in detecting the state of sleep, but less so in sensing wakefulness. For example, lying down could be misinterpreted as sleep due to the absence of movements, thereby leading to overestimation [13,23,32]. This deficiency is primarily due to the fact that actigraphy associates reduced movements with sleep.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

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A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Mahloko

Adebesin

2020

Lecture Notes in Computer Science

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The use of consumer wearable health device (CWHD) for fitness tracing has seen an upward trend worldwide. CWHDs support individuals in taking ownership of their personal well-being and keeping track of their fitness goals. However, there are genuine concerns over the accuracy of the data collected by these devices. In this study, we investigated the factors that influence the accuracy of the data collected by CWHDs for heart rate measurement, physical activity (PA), and sleep monitoring using a systematic literature review. Forty-seven papers were analyzed from five electronic databases based on specific inclusion and exclusion criteria. All 47 papers that we analyzed were published by authors from developed countries. Using thematic analysis, we classified the factors that influence the accuracy of the data collected by CWHDs into three main groups, namely (i) the tracker and sensor type, (ii) the algorithm used in the device, and (iii) the limitation in the design, energy consumption, and processing capability of the device. The research results point to a dearth of studies that focus on the accuracy of the data collected by CWHDs by researchers from developing countries. Keywords: Consumer wearable health device Á CWHD Á Wearable health device Á Wearable health device data accuracy Á Systematic literature review © IFIP International Federation for Information Processing 2020

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“…Over the past decades, many aBCI studies have been conducted for exploring novel methods of communication (e.g., mental speller, brain switch system, and robotic arm controller) in patients with locked-in syndrome [2]. Meanwhile, with the recent development of wearable EEG recording devices [4,5], increasing interest has been drawn toward pBCI applications in practical and daily-life scenarios [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Cha

Han

2020

Sensors

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With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

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Smart technologies toward sleep monitoring at home

Cited by 74 publications

References 72 publications

Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

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