Showing papers on "Sleep (system call) published in 2020"

BodyCompass: Monitoring Sleep Posture with Wireless Signals

Abstract: Monitoring sleep posture is important for avoiding bedsores after surgery, reducing apnea events, tracking the progression of Parkinson's disease, and even alerting epilepsy patients to potentially fatal sleep postures. Today, there is no easy way to track sleep postures. Past work has proposed installing cameras in the bedroom, mounting accelerometers on the subject's chest, or embedding pressure sensors in their bedsheets. Unfortunately, such solutions jeopardize either the privacy of the user or their sleep comfort. In this paper, we introduce BodyCompass, the first RF-based system that provides accurate sleep posture monitoring overnight in the user's own home. BodyCompass works by studying the RF reflections in the environment. It disentangles RF signals that bounced off the subject's body from other multipath signals. It then analyzes those signals via a custom machine learning algorithm to infer the subject's sleep posture. BodyCompass is easily transferable and can apply to new homes and users with minimal effort. We empirically evaluate BodyCompass using over 200 nights of sleep data from 26 subjects in their own homes. Our results show that, given one week, one night, or 16 minutes of labeled data from the subject, BodyCompass's corresponding accuracy is 94%, 87%, and 84%, respectively.

IoT Healthcare: Design of Smart and Cost-Effective Sleep Quality Monitoring System

Abstract: Getting quality sleep is important for every person to get better physical health. Irregular sleep patterns may indicate the illness resulting in chronic depression, which makes the evaluation of the sleep cycle mandatory for a healthy body and mind. In the arena of globalization, along with the increased facilities, various other challenges have been probed to provide the quality health care facilities with the use of economical instruments and technology. The development of the Internet of Things (IoT) technology purports the preambles to build a consistent and cost-effective system to monitor the sleep quality of patients. Several other systems are available for this purpose; however, such systems are very costly and difficult to implement. To overcome the issue, this study suggests an inventive system to monitor and analyze the sleep patterns using ambient parameters. The proposed system is effective enough that it can proficiently monitor patient’s sleep using Commercial off the Shelf (COS) sensors as well as predicts the results using the intelligent capability of the random forest model. The patient’s bio status including physical movement of the body, heartbeat, SPO2 level (oxygen saturation in the blood for the proper functioning of the body), and snoring patterns could be measured through this system, in which recorded data is transmitted to the computer system in a real-time environment. This system consists of two parts. One part consists of analyzing the behavior of data using the intelligent technique of the random forest model and decision rules in a real-time environment. This real-time analysis notifies the caretaker about the situation of the patient. In the second part, batch data processing is performed which allows the detailed analysis of data using statistical methods to produce the overall condition of the patient in a specified interval of time. Through the proposed system, we can easily measure the sleep patterns of patients and provide them with better treatment by using this simple and cost-effective system. The result of the conducted research shows that the proposed technique provides 95% accuracy. The patient’s sleep data is used to test this method through the validation of manual results, which provides the minimum error rate. This study highlights the implementation of an intelligent and smart sleep quality monitoring system using IoT on a variant number of people with minimum expense rate.

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Abstract: Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and over-fitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: “https://github.com/zhaoaite/SSRM”.

SleepGuardian: An RF-Based Healthcare System Guarding Your Sleep from Afar

Abstract: The ever accelerating process of urbanization urges more and more population into the swelling cities. While city residents are enjoying an entertaining life supported by advanced informatics techniques like 5G and cloud computing, the same technologies have also gradually deprived their sleep, which is crucial for their wellness. Therefore, sleep monitoring has drawn significant attention from both the research and industry communities. In this article, we first review the sleep monitoring issue and point out three essential properties of an ideal sleep healthcare system, that is, realtime guarding, fine-grained logging, and cost-effectiveness. Based on the analysis, we present SleepGuardian, a Radio Frequency (RF) based sleep healthcare system leveraging signal processing, edge computing and machine learning. SleepGuardian offers an offline sleep logging service and an online abnormality warning service. The offline service provides a fine-grained sleep log like timing and regularity of bed time, onset of sleep and night time awakenings. The online service keeps guarding the subject for any abnormal behaviors during sleep like intensive body twitches and a sudden seizure attack. Once an abnormality happens, it will automatically warn the designated contacts like a nearby emergency room or a close-by relative. We prototype SleepGuardian with low-cost WiFi devices and evaluate it in real scenarios. Experimental results demonstrate that SleepGuardian is very effective.

iSleep: A Smartphone System for Unobtrusive Sleep Quality Monitoring

Abstract: The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.

Sleep stage detection using only heart rate

Abstract: Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

Action plan interrupted: resolution of proactive interference while coordinating execution of multiple action plans during sleep deprivation.

Abstract: The ability to retain an action plan to execute another is necessary for most complex, goal-directed behavior Research shows that executing an action plan to an interrupting event can be delayed when it partly overlaps (vs does not overlap) with the retained action plan This phenomenon is known as partial repetition costs (PRCs) PRCs reflect proactive interference, which may be resolved by inhibitory, executive control processes We investigated whether these inhibitory processes are compromised due to one night of sleep deprivation Participants were randomized to a sleep-deprived group or a well-rested control group All participants performed an action planning task at baseline after a full night of sleep, and again either after a night of sleep deprivation (sleep-deprived group) or a full night of sleep (control group) In this task, two visual events occurred in a sequence Participants retained an action plan to the first event in working memory while executing a speeded action to the second (interrupting) event; afterwards, they executed the action to the first event The two action plans either partly overlapped (required the same hand) or did not (required different hands) Results showed slower responses to the interrupting event during sleep deprivation compared to baseline and the control group However, the magnitude of the PRCs was no different during sleep deprivation compared to baseline and the control group Thus, one night of sleep deprivation slowed global responses to the interruption, but inhibitory processes involved in reducing proactive interference while responding to an interrupting event were not compromised These findings are consistent with other studies that show sleep deprivation degrades global task performance, but does not necessarily degrade performance on isolated, executive control components of cognition The possibility that our findings involve local as opposed to central inhibition is also discussed

Editorial Perspective: Adolescents' fragile sleep-shining light on a time of risk to mental health

Abstract: As many other facets of life-biological, behavioral, psychological, cognitive, and social-undergo change during adolescence, so too does sleep. The context of sleep behavior is modified by alterations to underlying bioregulatory processes that challenge sleep's timing, regularity, and quantity. The buildup of sleep pressure during the day gets slower, opening the door for youth to stay awake later; however, the amount of sleep required does not diminish. Further, the circadian timing system delays, again providing the biological impetus for later sleep. When these changes meet societal demands for early wake, most teens cannot find a way to get enough sleep at a consistent time from night to night. Insufficient and irregular sleep provides a fragile foundation to support mental health.

The effect of complex cognitive training on subsequent night sleep

Abstract: Using a nap design, we have recently shown that training at a complex cognitive task at bedtime improves objective sleep quality by reducing sleep fragmentation. In order to extend our findings to nighttime sleep, here we assess the impact of a multi-componential cognitive task at bedtime on the subsequent sleep episode of subjects reporting habitual bad sleep, allegedly characterized by high sleep fragmentation. In a within-subjects design, 20 subjective bad sleepers underwent polysomnographic recording in three conditions: (a) baseline sleep (BL); (b) post-training sleep (TR), preceded by a complex ecological task, i.e. a modified version of the word game Ruzzle; (c) post-active control sleep (AC), preceded by a control task. Sleep in TR was more organized (higher number of cycles and longer time spent in cycles) and showed lower microarousal frequency than in AC and BL. As for sleep continuity (total and brief awakening frequency) and other stability measures (state transition and functional uncertainty period frequency, time in functional uncertainty), both TR and AC showed significant improvements compared with BL. Arousal frequency was also reduced in TR relative to BL. Our results show a clear impact of cognitive training on subsequent night sleep, basically consisting of an increase in sleep continuity, stability and organization. In our sample of bad sleepers, these post-training changes end up representing a notable sleep improvement, also consistently reflected in subjective sleep quality perception. Therefore, ecological pre-sleep cognitive training should be further studied as an easily accessible complementary approach in standard therapies for sleep-disordered populations.

SleepApp: Providing Contextualized and Actionable Sleep Feedback

Abstract: Most commercial sleep sensors typically rely on population-level data and focus on recommendations based on objective metrics such as sleep duration or sleep efficiency However, there is inter-individual trait-variability to sleep and people's sleep habits are individualized To prompt users to adopt habits that improve sleep health, meaningful sleep feedback must not only provide evidence of how users' behaviors affect their sleep quality, as objectified by some of the metrics, but also show how carry-over effects of sleep affect daytime cognitive function In this paper, we propose and validate an approach that combines both subjective and objective measures of sleep, accounting for a person's lifestyle and ties it to meaningful and measurable carryover effects such as daytime alertness and working memory Our approach is based on the medical community's Ru-SATED framework, which characterizes sleep through six dimensions: Regularity, Satisfaction, Alertness, Timing, Efficiency and Duration Using data collected by a smart phone app: SleepApp, with a suite of ecological momentary assessment tests from 9 participants over 14 days, we demonstrate how sleep health can be contextualized to the individual lifestyle and actionable feedback can be generated In a follow up survey with 57 respondents, we show how the actionable feedback generated by SleepApp can encourage in users the intent to make adjustments to their sleep habits that may impact their daytime cognitive function

Patient-Reported Outcomes for Measuring Sleep Disturbance in Pediatric Atopic Dermatitis: cross sectional study of PROMIS Pediatric Sleep Measures and Actigraphy.

Abstract: Background Most children with atopic dermatitis(AD) suffer from sleep disturbance, but reliable and valid assessment tools are lacking. Objectives To test PROMIS (Patient Reported Outcomes Measurement Information System) sleep measures in pediatric AD and to develop an algorithm to screen, assess and intervene to reduce sleep disturbance. Methods A cross-sectional study was conducted with AD children ages 5-17 years and one parent(n=61), who completed sleep, itch, and AD-specific questionnaires; clinicians assessed disease severity. All children wore actigraphy watches for 1-week-objective sleep assessment. Results PROMIS sleep disturbance parent-proxy-reliability was high (Cronbach's α=0.90) and differentiated among Patient Oriented Eczema Measure (POEM)-determined disease severity groups (mean±SD in mild vs. moderate vs. severe was 55.7±7.5 vs. 59.8±10.8 vs. 67.1±9.5, p Limitations This was a local sample. Conclusions Sleep disturbance in pediatric AD should be screened using the POEM sleep question, with further assessment using the PROMIS sleep disturbance measure or objective sleep monitoring if needed.

Smart alarm based on sleep stages prediction

Abstract: A sleep inertia after waking up strongly affects the overall mental recovery after sleep. The sleep inertia depends not as much on overall sleep quality but also on the sleep stage in the waking up moment. The fix-time alarming system results in waking up at random sleep stage, which results in frequent sleep inertia. The widely used flow-time alarming systems based on motion detection (actigraphy) reduce but do not eliminate sleep inertia. Such systems do not wake up users in Deep sleep stage, but may instead wake them up in Wake, Light, or REM (Rapid Eyes Movement) stages. Moreover, frequent waking up in the REM stage results in serious psychological issues.We present a smartwatch alarm system that predicts sleep stages and thus produces an alarm call at an easy waking up moment with minimal sleep inertia effect. The sleep stages are predicted using an Encoder-Decoder Recurrent Neural Network model. The rationale of the prediction is that each sleep stages cycling pattern is a continuous quasi-periodic process. Experimental results from over 138 nocturnal sleep periods from 92 respondents show that our system provides 66-70% accuracy for Deep, Light, Wake, REM sleep stages and 71-77% accuracy for 2-classes (Deep/REM vs. Light/Wake stages) prediction classifications. The proposed alarm system wakes up the user at the moment when Easy Wake (Wake/Light) stage is the most probable.

A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device

Abstract: The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient's home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.

Research on freshman and sleeping habits: A text message-based sleep intervention

Abstract: Objective: Lack of understanding regarding the function of sleep and lack of education regarding healthy sleep practices may hinder college students from getting sufficient quality sleep. The current study examines the effect of a text message based educational intervention aimed at improving sleep quality and sleep hygiene behaviors in freshman undergraduate college students.Participants: 135 undergraduate students were recruited fall of 2016.Methods: Three discussion groups were held to test and refine the text message content. Students were randomized into a three-group pretest–posttest experimental design. Participants completed measures of sleep quality, sleep hygiene, and sleep knowledge.Results: Data analysis indicated the intervention did not demonstrate significant differences between groups over time on sleep quality, sleep hygiene behaviors, and sleep knowledge.Conclusion: More research is needed to understand how best to harness text messaging technology and sleep health education to p...

The effect of computer game playing habits of university students on their sleep states.

Abstract: Objective The aim of this study is to evaluate the effect of computer game playing habits of university students on their sleep states. Design and methods The study was conducted cross-sectionally with the online survey method. Finding In this study, it was determined that the students who played games for an average of ≥2 hours per day had later bedtime and later wake-up time, poorer sleep quality, and higher daytime sleepiness. It was found that as the level of game addiction increased, sleep quality decreased, the severity of daytime sleepiness increased, and the wake-up time shifted to a later time. Practice implications Nurses should develop effective intervention strategies involving technology management and sleep hygiene studies to reduce game-playing time of students.

Signal Processing based Autonomous Sensory Meridian Response to Treat Insomnia

Abstract: Insomnia is a sleep disorder that causes a disruption in sleep patterns where the affected person toils to sleep off or to remain asleep. The signals generated from the brain are also affected because of the irregularity in sleep patterns. The identification of sleep disorder in India currently is a clinical interview by the doctor which is subjective and suffers from human error judgment. Therefore, an additional reliable and correct diagnostic tool area unit required to assist the doctor in creating calls. The aim of this work is to diagnose Insomnia using single channel Electroencephalogram (EEG) signal and to suggest ASMR sounds to enhance sleep quality.

The relationship between mindfulness, PTSD-related sleep disturbance, and sleep quality: Contributions beyond emotion regulation difficulties.

Abstract: Objective Emotion regulation difficulties have been associated with traumatic event exposure, posttraumatic stress disorder (PTSD) symptoms, and associated sleep disturbances. Dispositional mindfulness, the tendency to experience the present moment, on purpose with acceptance and nonjudgment, can be conceptualized as adaptive emotion regulation. While dispositional mindfulness has been associated with adaptive posttrauma outcomes, it has not been examined in relation to trauma sequelae, such as sleep disturbance. The current study aimed to expand upon previous research to further explore the relationship between dispositional mindfulness and trauma sequelae. Method Participants (N = 217) were recruited using Amazon's Mechanical Turk to complete online surveys assessing PTSD symptom severity (PTSS), sleep disturbance, emotion regulation difficulties, and mindfulness. Results After controlling for PTSS, the results suggest that dispositional mindfulness is associated with lower frequency of PTSD-related sleep disturbance and better sleep quality (daily disturbances). Additionally, the results suggest that specific dispositional mindfulness domains remain significant when emotion regulation difficulties domains were included in the model. Conclusions Future research should further examine this relationship to inform mindfulness-based interventions for PTSD and sleep disturbance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A Smartphone-based Sleep Monitoring Model

Abstract: Sleep is a very important physiological activity in life. The quality of sleep directly affects people ’s health. At present, common sleep monitoring products from smart mattresses to smart bracelets, although they can monitor sleep, must either be laid on the bed or must be worn throughout the night, which does not provide a good user experience. At the same time, such products mainly rely on the single factor of physical activity to judge sleep, and many details of the sleep process cannot be discussed in depth, such as breathing and snoring. In order to solve the above problems, this paper proposes a non-contact human sleep monitoring method, which judges sleep staging by two aspects of body motion and respiration and compares it with traditional wristband products to verify the effectiveness of the method.

No-show rates to a sleep clinic: drivers and determinants.

Abstract: Study Objectives:Attendance to sleep clinic appointments is imperative to diagnose sleep-related disorders and to offer appropriate treatment. As part of our quality assurance program, we assessed ...

Classifying sleep states using persistent homology and Markov chain: a Pilot Study

Abstract: Obstructive sleep Apnea (OSA) is a form of sleep disordered breathing characterized by frequent episodes of upper airway collapse during sleep. Pediatric OSA occurs in 1-5% of children and can related to other serious health conditions such as high blood pressure, behavioral issues, or altered growth. OSA is often diagnosed by studying the patient's sleep cycle, the pattern with which they progress through various sleep states such as wakefulness, rapid eye-movement, and non-rapid eye-movement. The sleep state data is obtained using an overnight polysomnography test that the patient undergoes at a hospital or sleep clinic, where a technician manually labels each 30 second time interval, also called an "epoch", with the current sleep state. This process is laborious and prone to human error. We seek an automatic method of classifying the sleep state, as well as a method to analyze the sleep cycles. This article is a pilot study in sleep state classification using two approaches: first, we'll use methods from the field of topological data analysis to classify the sleep state and second, we'll model sleep states as a Markov chain and visually analyze the sleep patterns. In the future, we will continue to build on this work to improve our methods.

Towards Improving Sleep Quality Using Automatic Sleep Stage Classification and Binaural Beats

Abstract: Sleep disorders are extremely common in today’s society and are greatly affecting the health and safety of every person suffering from one. Over the last decades, Automatic Sleep Stage Classification (ASSC) systems have been developed to assist specialists in the sleep stage scoring process and therefore in the diagnosis of sleep disorders. Binaural beats are auditory phenomena that have been shown to have a positive impact in sleep quality and mental state. This paper introduces a framework that combines an ASSC system and a binaural beats generator in real time. Our goal is to pave the way for developing systems which could reproduce specific binaural beats depending on the detected sleep stage, in order to entrain the brain into a more efficient sleep. For the ASSC stage, different classifiers were evaluated using data signals retrieved from a public sleep stage signals database, corresponding to ten subjects. The complete framework was tested using the database signals and signals from a test subject, captured and processed in real time. Our proposed framework may lead to a fully automated system to improve sleep quality without the need of medication.

Feasibility and utility of enhanced sleep management on in-patient psychiatry wards.

Abstract: Aims and method Sleep disturbance is common in psychiatry wards despite poor sleep worsening mental health. Contributory factors include the ward environment, frequent nightly checks on patients and sleep disorders including sleep apnoea. We evaluated the safety and feasibility of a package of measures to improve sleep across a mental health trust, including removing hourly checks when safe, sleep disorder screening and improving the ward environment. Results During the pilot there were no serious adverse events; 50% of in-patients were able to have protected overnight sleep. Hypnotic issuing decreased, and feedback from patients and staff was positive. It was possible to offer cognitive–behavioural therapy for insomnia to selected patients. Clinical implications Many psychiatry wards perform standardised, overnight checks, which are one cause of sleep disruption. A protected sleep period was safe and well-tolerated alongside education about sleep disturbance and mental health. Future research should evaluate personalised care rather than blanket observation policies.

N=1 Modelling of Lifestyle Impact on SleepPerformance.

Abstract: Sleep is critical to leading a healthy lifestyle Each day, most people go to sleep without any idea about how their night's rest is going to be For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it Despite current research, creating personalized sleep models in real-world settings has been challenging Existing literature provides several connections between daily activities and sleep quality Unfortunately, these insights do not generalize well in many individuals For these reasons, it is important to create a personalized sleep model This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and their impact on sleep quality Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework These insights can be used by the user or a personal health navigator to provide guidance in improving sleep

In-Sleep Activity Detecting Algorithm for Sleepcare System

Abstract: High sleep quality enhances life while helping to improve health. Sleep quality increases with fewer sleep disorders such as in-sleep activity is assessed based on such sleep disorders as indicators. In this context, the measurement of in-sleep activity will enable the analysis of sleep quality. This study suggests an algorithm to measure the number of in-sleep activity using a pressure sensor. Movements are detected using the vibration data that is measured from the sensor during tossing and turning, and the pattern of changing pressure according to person's posture in sleep is analyzed. Measuring in-sleep activity using this algorithm will enable the evaluation of sleep quality. Also, to evaluate the algorithm, an actual test environment was constructed where in-sleep activities were measured, and it was determined whether they were counted accurately. As a result, it presented an average cognitive accuracy of 98.6%, and the availability of the algorithm was confirmed.

Sleep Performance and Physical Activity Estimation from Multisensor Time Series Sleep Environment Data.

Abstract: Sleep is an essential physiological function, needed for the proper functioning of the brain and therefore for the general well-being and for a good quality of life. Currently, more and more people use mobile and wearable devices to monitor their sleep and physical activity. But while some of those recent devices may even provide reliable measurements of sleep structure, they do not provide a consolidated and definite answer for what has contributed to that situation. With the present study, we intend to verify and establish relationships between some environmental factors, such as temperature, humidity, luminosity, noise or air quality parameters and between sleep performance and physical activity and assert legal issues with GRPD law. A multisensor monitoring system was used to obtain a real dataset consisting of 55 night sessions of time series sleep environment data. We have also explored the feasibility of using time series machine learning models to predict sleep stages and to estimate the level of physical activity of a person.

Sleep Tracker and Smartphone: Strengths and Limits to Estimate Sleep and Sleep-Disordered Breathing

Abstract: An increasing number of customers are using wearable devices, sleep trackers, or smartphones apps to monitor and measure a variety of body functions. These devices claim to measure different physiological parameters such as sleep quality, snoring, or sleep-disordered breathing (SDR). Here, we present a review of validation studies of sleep applications to providing some guidance in terms of their reliability to assess sleep in healthy and clinical populations. A review was conducted on PubMed. Twelve validation studies were identified, evaluating sleep trackers and smartphone app performances compared to polysomnography (PSG) or actigraphy for sleep assessment in healthy and clinical samples. Validation studies in healthy children, adolescent, and adult show that sleep trackers overestimate sleep time, sleep efficiency, and the latency to fall asleep. “Jawbone UP 3” and “Fitbit Charge” sleep trackers show good equivalence with the sleep diary total sleep time (effect size = 0.09 and 0.23, respectively). Compared to electrocardiography in determining HR during sleep, the “Fitbit Charge 2” reports no significant difference in the mean HR (0.09 beats per minute, P = 0.426). Most of the smartphone apps based on body movements, measured by accelerometers, show a weak correlation between PSG and apps sleep parameters. A multiple parameter-based smartphone app using the EarlySense contact-free sleep monitoring system shows that total sleep time estimates with the contact-free system were closely correlated with PSG. More experimental studies are warranted to assess the validity of sleep trackers or smartphones apps for clinical applications and their reliability in sleep–wake detection particularly.