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EZwakeup: A sleep environment design for sleep quality improvement
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Sleep quality affects people's work performance, mood, safety, and quality of life. Poor sleep quality reduces short-term memory, cognitive abilities, and motor skills for all age groups. We introduce EZwakeup, a system that extracts sleep quality indicators with an eTextile-based sensing system and applies feedback-guided external stimuli to smoothly wake people up from deep sleep. It does not require people to wear any external devices and can be directly deployed unobtrusively in home environment. In experiments, participants reported that they felt well rested and energetic for the rest of the day when they were awakened by the guided stimuli. This result suggests that EZwakeup might be a viable option for improving personal sleep quality and have potential in treating common sleep disorders.
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... In a different approach, Lullaby [26] and EZwakeup [27] use a set of heterogeneous sensors around the bed to get sleep-related data with little or no user interaction. One of the most objectionable things about these projects is that additional infrastructure is necessary to implement the systems. ...
... Based on Figure 1, we conclude that the most obtrusive systems are the ones proposed in [14], [23] and [24] due to users sleeping in an unnatural environment with perceptible sensors. On the other hand, [13], [16], [17]and [27] are systems that can be considered unobtrusive. We are aware that the nature and goals of the systems are different; however, we are only comparing unobtrusiveness, not system efficiency, which is the other half of an evaluation. ...
... Finally, the system does not require a wearable device, it does not track activities during the day and the user can sleep in a natural environment. Low Obtrusiveness (6/7) Medium Obtrusiveness (5/7) Obtrusive (4/7) Hao et al. [13] Fahim et al. [16] Daskalova et al. [17] Huang et al. [27] Bai et al. [18] Bauer et al. [22] Krishna et al. [11] Chen et al. [15] Pombo & Garcia [19] Paalasmaa et al. [20] Papakostas et al. [21] Han et al. [25] Ren et al. [14] Min et al. [23] Lawson et al. [24] Kay et al. [26] ...
Unobtrusiveness is one of the main issues concerning health-related systems.
Many developers affirm that their systems do not burden users; however, this
is not always achieved. This article evaluates the obtrusiveness of various
systems developed to improve sleep quality. The systems analyzed are
related to sleep hygiene, since it has become an interesting topic for
researchers, physicians and people in general, mainly because it has become
part of the methods used to estimate a persons’ health status A set of design
elements are presented as keys to achieving unobtrusiveness. We propose a
scale to measure the level of unobtrusiveness and use it to evaluate several
systems, with a focus on smartphone applications
... In a different approach, Lullaby [26] and EZwakeup [27] use a set of heterogeneous sensors around the bed to get sleep-related data with little or no user interaction. One of the most objectionable things about these projects is that additional infrastructure is necessary to implement the systems. ...
... Based on Figure 1, we conclude that the most obtrusive systems are the ones proposed in [14], [23] and [24] due to users sleeping in an unnatural environment with perceptible sensors. On the other hand, [13], [16], [17]and [27] are systems that can be considered unobtrusive. We are aware that the nature and goals of the systems are different; however, we are only comparing unobtrusiveness, not system efficiency, which is the other half of an evaluation. ...
... Finally, the system does not require a wearable device, it does not track activities during the day and the user can sleep in a natural environment. Low Obtrusiveness (6/7) Medium Obtrusiveness (5/7) Obtrusive (4/7) Hao et al. [13] Fahim et al. [16] Daskalova et al. [17] Huang et al. [27] Bai et al. [18] Bauer et al. [22] Krishna et al. [11] Chen et al. [15] Pombo & Garcia [19] Paalasmaa et al. [20] Papakostas et al. [21] Han et al. [25] Ren et al. [14] Min et al. [23] Lawson et al. [24] Kay et al. [26] ...
Unobtrusiveness is one of the main issues concerning health-related systems. Many developers affirm that their systems do not burden users; however, this is not always achieved. This article evaluates the obtrusiveness of various systems developed to improve sleep quality. The systems analyzed are related to sleep hygiene, since it has become an interesting topic for researchers, physicians and people in general, mainly because it has become part of the methods used to estimate a persons’ health status A set of design elements are presented as keys to achieving unobtrusiveness. We propose a scale to measure the level of unobtrusiveness and use it to evaluate several systems, with a focus on smartphone applications. Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved.
... Subjective methods include Pittsburgh Sleep Quality Index (PSQI) [29] instrument that is widely used in clinical settings for rough evaluation of sleep quality. Objective method refers to the analysis of sleep characterized by a set of metrics called sleep structure [20,30]. Some of the sleep metrics could only be measured using polysomnography in sleep clinics, while others could be monitored using commercial sleep trackers at acceptable accuracy level. ...
... • Correlation analysis: Spearman correlation coefficients were calculated for each pair of variables with missing values pairwisely removed. We used Spearman correlation rather than Pearson correlation mainly for the following two reasons: (1) The relationships between sleep and potential contributing factors are not [3]; Toss 'N' Turn [4]; iSleep [11]; Poster [12] Measuring sleep stage and sleep duration Best Effort Sleep Model [13]; SleepHunter [14] Recognizing sleep patterns Sleep pattern analysis system [15,16] Understanding sleep environment Lullaby [8]; combining wearable environmental sensing [17] Improving sleep quality and promoting sleep health ShutEye [18]; the Wearable Lullaby [19]; EZwakeup [20]; opportunities for computing technologies to support healthy sleep behavior [7] Reliving sleep disorders SNORES [21]; ZZZoo Pollows [22]; sleep apnea detection [23] Exploring relationships to contributing factors SleepTight [24] Others Sleep quality prediction: SleepMiner [25]; social alarm clock: Somnometer [26]; nap supporting system [27]; Sleepwalk supporting system: Sleepstellar [28] Pers Ubiquit Comput (2016) 20:985-1000 987 necessarily linear, e.g., previous studies suggest that the relationship between sleep and exercise is nonlinear [35]; (2) variables in our study were ordinal instead of being continuous. Therefore, only Spearman correlation is suitable for the analysis. ...
... In order to help users make sense of the correlations between their sleep and contextual factor, SleepExplorer also helps them understand the sleep quality through the concept of sleep structure [20,30]. It is widely recognized in sleep research community that normal sleep is difficult to define because individuals vary enormously, differing in physiology, psychology, lifestyle, and living environment [49,50]. ...
Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.
... Sleep sensing has been a growing field, where sleep technology research has put considerable effort into exploring how and when people track their sleep, often by looking for less obtrusive ways to bio-track to minimise disruption of sleep. This has been approached by leveraging smartphone sensors [16,93], smart textiles [31], personal radar systems [69], data-driven personal sleep recommendations [20], or even dreams [7,12,28]. While mostly designed for individuals, sleep tracking has been extended to research sleep environment tracking [35] and multi-user health tracking [65]. ...
With growing interest in how technology can make sense of our body and bodily experiences, this work looks at how these experiences are communicated through and with the help of technology. We present the ways in which knowledge about sleep, and how to manipulate it, is collectively shared online. This paper documents the sleep-change practices of four groups of 'Sleep Hackers' including Nurses, Polyphasic Sleeper, Over-sleepers, and Biohackers. Our thematic analysis uses 1002 posts taken from public forums discussing sleep change. This work reveals the different ways individuals share their experiences and build communal knowledge on how to 'hack' their sleep -- from using drugs, external stimulation, isolation, and polyphasic sleeping practices where segmented sleep schedules are shared between peers. We describe how communal discussions around the body and sleep can inform the development of body sensing technology. We discuss the opportunities and implications for designing for bodily agency over sleep changes both in relation to collaboratively developed understandings of the body and social context of the user. We also discuss notions of slowly changing bodily processes and sensory manipulation in relation to how they can build on the exploration of soma-technology.
... Similar applications strive to consolidate users' sleep schedule with their natural Circadian rhythm, such as the Philips Wake-up Light [1]. The EZwakeup sleep environment wakes people up slowly and smoothly from deep sleep [31]. ...
... The visualization is critical in many systems, e.g. health [5,6], medical [4], and data mining [9]. ...
Machine learning (ML) is believed to be an effective and efficient tool to build reliable prediction model or extract useful structure from an avalanche of data. However, ML is also criticized by its difficulty in interpretation and complicated parameter tuning. In contrast, visualization is able to well organize and visually encode the entangled information in data and guild audiences to simpler perceptual inferences and analytic thinking. But large scale and high dimensional data will usually lead to the failure of many visualization methods. In this paper, we close a loop between ML and visualization via interaction between ML algorithm and users, so machine intelligence and human intelligence can cooperate and improve each other in a mutually rewarding way. In particular, we propose "transparent boosting tree (TBT)", which visualizes both the model structure and prediction statistics of each step in the learning process of gradient boosting tree to user, and involves user's feedback operations to trees into the learning process. In TBT, ML is in charge of updating weights in learning model and filtering information shown to user from the big data, while visualization is in charge of providing a visual understanding of ML model to facilitate user exploration. It combines the advantages of both ML in big data statistics and human in decision making based on domain knowledge. We develop a user friendly interface for this novel learning method, and apply it to two datasets collected from real applications. Our study shows that making ML transparent by using interactive visualization can significantly improve the exploration of ML algorithms, give rise to novel insights of ML models, and integrates both machine and human intelligence.
In situ self-report is widely used in human-computer interaction, ubiquitous computing, and for assessment and intervention in health and wellness. Unfortunately, it remains limited by high burdens. We examine unlock journaling as an alternative. Specifically, we build upon recent work to introduce single slide unlock journaling gestures appropriate for health and wellness measures. We then present the first field study comparing unlock journaling with traditional diaries and notification based reminders in self report of health and wellness measures. We find unlock journaling is less intrusive than reminders, dramatically improves frequency of journaling, and can provide equal or better timeliness. Where appropriate to broader design needs, unlock journaling is thus an overall promising method for in situ self report.
Sleep constitutes a big portion of our lives and is a major part of health and well-being. Monitoring the quality of sleep can aid in the medical diagnosis of a variety of sleep and psychiatric disorders and can serve as an indication of several chronic diseases. Sleep stage analysis plays a pivotal role in the evaluation of the quality of sleep and is a proven biometric in diagnosing cardiovascular disease, diabetes, and obesity [32]. We describe an unobtrusive framework for sleep stage identification based on a high-resolution pressure-sensitive e-textile bed sheet. We extract a set of sleep-related biophysical and geometric features from the bed sheet and use a two-phase classification procedure for Wake—Non Rapid Eye Movement—Rapid Eye Movement stage identification. A total of seven all-night polysomnography recordings from healthy subjects were used to validate the proposed bed sheet system and the ability to extract sleep stage information from it. When compared with the gold standard, the described system achieved 70.3% precision and 71.1% recall on average. These results suggest that unobtrusive sleep macrostructure analysis could be a viable option in clinical and home settings in the near future. Compared with existing techniques for sleep stage identification, the described system is unobtrusive, fits seamlessly into the user's familiar sleep environment, and has additional advantages of comfort, low cost, and simplicity.
The monitoring of human respiratory rate is essential in many clinical applications including the detection and monitoring of sleep disorders, the monitoring of newborns for Sudden Infant Death Syndrome (SIDS), and identifying patients at high risk up to 24 hours before an adverse event like stroke and cardiac arrest [1]. Traditional noninvasive respiratory rate measurements in a hospital setting rely on clinical staff to visually track a patient's chest movement for a period of time to derive the respiratory rate from the number of movements observed. Failure to perform continuous and quantified measurements of respiratory rate could result in an inability to rescue a patient exhibiting respiratory distress. Severe after effects hinder recovery and result in loss of time, cost, or even life. This paper proposes an e-textile pressure sensitive bed sheet to non-invasively and accurately measure respiratory rate by analyzing time-stamped pressure distribution sequences. The bed sheet provides a 24/7 quantified on-bed respiratory rate monitoring service. It is made of e-textile and is similar to a regular bed sheet in comfort. As a result, it can seamlessly fit in common clinical or home environments, reducing the possible interference with a patient's regular sleeping habits and resulting in a type of inconspicuous monitoring.
The bedroom environment can have a significant impact on the quality of a person's sleep. Experts recommend sleeping in a room that is cool, dark, quiet, and free from disruptors to ensure the best quality sleep. However, it is sometimes difficult for a person to assess which factors in the environment may be causing disrupted sleep. In this paper, we present the design, implementation, and initial evaluation of a capture and access system, called Lullaby. Lullaby combines temperature, light, and motion sensors, audio and photos, and an off-the-shelf sleep sensor to provide a comprehensive recording of a person's sleep. Lullaby allows users to review graphs and access recordings of factors relating to their sleep quality and environmental conditions to look for trends and potential causes of sleep disruptions. In this paper, we report results of a feasibility study where participants (N=4) used Lullaby in their homes for two weeks. Based on our experiences, we discuss design insights for sleep technologies, capture and access applications, and personal informatics tools.
This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.
This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.
Sleep plays a pivotal role in the quality of life, and sleep posture is related to many medical conditions such as sleep apnea. In this paper, we design a dense pressure-sensitive bedsheet for sleep posture monitoring. In contrast to existing techniques, our bedsheet system offers a completely unobtrusive method using comfortable textile sensors. Based on high-resolution pressure distributions from the bedsheet, we develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. We run a pilot study and evaluate the performance of our methods with 14 subjects to analyze 6 common postures. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than state-of-the-art methods, achieving up to 83.0% precision and 83.2% recall on average.
Sleep is a basic physiological process essential for good health. However, 40 million people in the U.S. are diagnosed with sleep disorders, with many more undiagnosed. To help address this problem, we developed an application, ShutEye, which provides a peripheral display on the wall-paper of the user's mobile phone to promote awareness about recommended activities that promote good sleep quality. Based on preferences about the user's desired bed-time and activities' for example - consuming caffeine or performing vigorous exercise - ShutEye displays guidance about when engaging in those activities is likely to affect sleep without requiring any explicit interaction from the user. In this paper, we describe ShutEye and results from a four-week field study with 12 participants. Results indicate that a simple, recommendation-based peripheral display can be a very low-effort but still effective method for improving awareness of healthy sleep habits. We also provide recommendations about designing peripheral displays and extend insights for designing health-based mobile applications.
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.
Getting the right amount of quality sleep is a key aspect of good health, along with a healthy diet and regular exercise. Human-computer interaction (HCI) researchers have recently designed systems to support diet and exercise, but sleep has been relatively under-studied in the HCI community. We conducted a literature review and formative study aimed at uncovering opportunities for computing to support the important area of promoting healthy sleep. We present results from interviews with sleep experts, as well as a survey (N = 230) and interviews with potential users (N = 16) to indicate what people would find practical and useful for sleep. Based on these results, we identify a number of design considerations, challenges, and opportunities for using computing to support healthy sleep behaviors, as well as a design framework for mapping the design space of technologies for sleep.
Impaired fear extinction and disturbed sleep coincide in post-traumatic stress disorder (PTSD), but the nature of this relationship is unclear. Rapid eye movement (REM) sleep deprivation impairs fear extinction recall in rodents and young healthy subjects, and animal models have demonstrated both disrupted sleep after fear conditioning and normalized sleep after extinction learning. As a correlation between unconditioned stimulus (US) responding and subsequent sleep architecture has been observed in healthy subjects, the goal of this study was to test whether US intensity would causally affect subsequent sleep. Twenty-four young healthy subjects underwent a fear conditioning session with skin conductance response measurements before an afternoon session of polysomnographically recorded sleep (up to 120 min) in the sleep laboratory. Two factors were manipulated experimentally in a 2 × 2 design: US (electrical shock) was set at high or low intensity, and subjects did or did not receive an extinction session after fear conditioning. We observed that neither factor affected REM sleep amount, that high US intensity nominally increased sleep fragmentation (more Stage 1 sleep, stage shifts and wake after sleep onset), and that extinction increased Stage 4 amount. Moreover, reduced Stage 1 and increased Stage 4 and REM sleep were associated with subjective sleep quality of the afternoon nap. These results provide evidence for the notion that US intensity and extinction affect subsequent sleep architecture in young healthy subjects, which may provide a translational bridge from findings in animal studies to correlations observed in PTSD patients.