iPillow: Sleep Quality Improvement System (original) (raw)
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International Journal of Emerging Trends in Engineering Research, 2020
Nearly one-third of human life is spent on sleeping, an easily reversible state of relative unresponsiveness and stillness which occurs more or less regularly and cyclically each day. A great sleep can subsidize countless positive outcomes, like better overall health, less sluggishness during the day, along with better mental health. The kind of sleep affects the health of the patient. Monitoring vital functions, like body temperature and sweating during sleep, is very important for sleep monitoring and clinical diagnosis. Smart pillows provide a relatively easy way to control sleep by placing humidity sensors and temperature in strategic locations. The patient's daily sleep information is very helpful in making decisions about diagnosis and treatment. Consumers can check their data with healthcare providers and gradually change their sleep habits. One can keep a check on their body condition when they sleep under various environmental parameters (humidity, brightness and temperature). The results indicate that one sleeps best in a dark and cool environment.
Future Internet
Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can help improve the quality of people’s sleep at a low cost. We reviewed the sleep quality of a group of people by analyzing their good and bad sleeping habits. We take that information to feed a proposed algorithm for a non-invasive sensor network in the person’s room for monitoring factors that help them fall asleep. We analyze vital signs and health conditions in order to be able to relate these parameters to the person’s way of sleeping. We help people get valuable information about their sleep with technology to live a healthy life, and we get about a 15% improvement in sleep quality. Finally, we compare the implementations given by the network with wearables to show the improvement in the behavior of the person...
A Smart Bed for Non-Obtrusive Sleep Analysis in Real World Context
IEEE Access
Sleep disorders are common health problems in industrialized societies and may be caused by underlying health issues. Current methods to assess the quality of sleep are invasive and not suitable for continuous monitoring in real world contexts. We have developed a smart sensing solution for non invasive sleep monitoring specifically conceived for the early identification of pre-clinical sleep disorders and insomnia in the general population. Our prototype, named the Smart-Bed, is a low-cost solution that gathers and processes data on the movement and position of the subject, physiological signals, and environmental parameters. Our tests on the prototype in controlled lab conditions highlighted that the mattress can reliably detect subject's position/motion, heart rate and breathing activity. It performs well compared to polysomnography and correctly classifies four behavioural conditions (no bed occupancy, wakefulness, non-REM sleep, and REM sleep), which are the basis for creating an objective sleep quality index. INDEX TERMS Accelerometers, piezoresistive devices, physiology, signal analysis, psychology, sleep monitoring, real world data.
IEEE Access
This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including ''sleep monitoring'', ''sleep monitoring technologies'', ''non-contact sleep monitoring'', ''mobile apps for sleep monitoring'', ''AI in sleep technologies'', and ''automated sleep staging.'' The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.
Toss ‘n’ Turn: Smartphone as Sleep and Sleep Quality Detector
The rapid adoption of smartphones along with people’s propensity to keep thee in their bedrooms at night presents an opportunity to use this device as a sleep detector. This ability is valuable for UbiComp systems in terms of user context, for personal informatics, and for healthcare as sleep is correlated with many health issues. To assess this opportunity, we collected one month of phone sensor and sleep diary entries from 27 people who have a variety of sleep contexts. We used this data to construct models that detect sleep and wake states, daily sleep quality, and global sleep quality. Our system classifies sleep state with 93.06% accuracy, daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48% accuracy. Individual-user models performed better than generally trained models; to expect reasonably good prediction performance of the individual models, a user has to manually supply 3 days of training data for sleep detection and 3 weeks of training data for quality inference. Best-performing features included noise peaks and movement variations.
IOP Conference Series: Materials Science and Engineering, 2017
View the article online for updates and enhancements. You may also like Actigraphy combined with EEG compared to polysomnography in sleep apnea patients Ingo Fietze, Thomas Penzel, Markku Partinen et al.-Markov modeling of sleep stage transitions and ultradian REM sleep rhythm Akifumi Kishi, Ikuhiro Yamaguchi, Fumiharu Togo et al.-Estimating sleep parameters using nasal pressure signals applicable to continuous positive airway pressure devices Jong-Uk Park, Urtnasan Erdenebayar, Eun-Yeon Joo et al.
Nature and Science of Sleep
The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions. Materials and Methods: Data collection spanned 98 separate nights of ad libitum sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device. Results: Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval);
代表著作1 Development and Evaluation of a Wearable Device for Sleep Quality Assessment
—Objective: In this study, a wearable actigraphy recording device with low sampling rate (1 Hz) for power saving and data reduction and a high accuracy wake-sleep scoring method for the assessment of sleep were developed. Methods: The developed actigraphy recorder was successfully applied to overnight recordings of 81 subjects with simultaneous PSG measurements. The total length of recording reached 639.8 hours. A wake-sleep scoring method based on the concept of movement density evaluation and adaptive windowing was proposed. Data from subjects with good (N=43) and poor (N=16) sleep efficiency (SE) in the range of 52.7%-97.42% were used for testing. The Bland–Altman technique was used to evaluate the concordance of various sleep measurements between the manual PSG scoring and the proposed actigraphy method. Results: For wake-sleep staging, the average accuracy, sensitivity, specificity, and kappa coefficient of the proposed system were 92.16%, 95.02%, 71.30%, and 0.64, respectively. For the assessment of SE, the accuracy of classifying the subject with good or poor SE reached 91.53%. The mean biases of SE, sleep onset time (SOT), wake after sleep onset (WASO) and total sleep time (TST) were-0.95%, 0.74 min, 2.84 min, and-4.3 min, respectively. Conclusion: These experimental results demonstrate the robustness and reliability of our method using limited activity information to estimate wake-sleep stages during overnight recordings. Significance: The results suggest that the proposed wearable actigraphy system is practical for the in-home screening of objective sleep measurements and objective evaluation of sleep improvement after treatment.
Lecture Notes in Computer Science, 2014
Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOGbased sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83 % accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI '13, 2013
There is an identified need for objective, reliable, and scalable methods of measuring and recording sleep. Such methods must be designed for easy integration into people's lives in order to support both sleep therapy and everyday personal informatics. This paper describes the design and evaluation of a mobile phone application to record sleep, the design of which has substantive foundation in clinical sleep research. Two user studies were carried out which demonstrate that the application produces valid measurements of sleep quality and high levels of usability, whilst not seriously disturbing sleep or the sleep environment. These findings suggest that the app is suitable for both everyday sleep monitoring in a personal informatics context, and for integration into sleep interventions.