Showing papers on "Sleep (system call) published in 2015"

A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer.

Abstract: Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012–2013) participants aged 60–83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a “count” based, device specific method.

Fine-grained sleep monitoring: Hearing your breathing with smartphones

Abstract: Sleep monitoring has drawn increasingly attention as the quality and quantity of the sleep are important to maintain a person's health and well-being. For example, inadequate and irregular sleep are usually associated with serious health problems such as fatigue, depression and cardiovascular disease. Traditional sleep monitoring systems, such as PSG, involve wearable sensors with professional installations, and thus are limited to clinical usage. Recent work in using smartphone sensors for sleep monitoring can detect several events related to sleep, such as body movement, cough and snore. Such coarse-grained sleep monitoring however is unable to detect the breathing rate which is an important vital sign and health indicator. This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones. Our system exploits the readily available smartphone earphone placed close to the user to reliably capture the human breathing sound. Given the captured acoustic sound, our system performs noise reduction to remove environmental noise and then identifies the breathing rate based on the signal envelope detection. Our system can further detect detailed sleep events including snore, cough, turn over and get up based on the acoustic features extracted from the acoustic sound. Our experimental evaluation of six subjects over six months time period demonstrates that the breathing rate monitoring and sleep events detection are highly accurate and robust under various environments. By combining breathing rate and sleep events, our system can provide continuous and noninvasive fine-grained sleep monitoring for healthcare related applications, such as sleep apnea monitoring as evidenced by our experimental study.

Sleep monitoring system using Kinect sensor

Abstract: Sleep activity is one of crucial factors for determining the quality of human life. However, a traditional sleep monitoring system onerously requires many devices to be attached to human body for achieving sleep related information. In this paper, we proposed and implemented the sleep monitoring system which can detect the sleep movement and posture during sleep using a Microsoft Kinect v2 sensor without any body attached devices. The proposed sleep monitoring system can readily gather the sleep related information that can reveal the sleep patterns of individuals. We expect that the analyzed sleep related data can significantly improve the sleep quality.

The role of sleep in motor sequence consolidation: stabilization rather than enhancement.

Abstract: Sleep supports the consolidation of motor sequence memories, yet it remains unclear whether sleep stabilizes or actually enhances motor sequence performance. Here we assessed the time course of motor memory consolidation in humans, taking early boosts in performance into account and varying the time between training and sleep. Two groups of subjects, each participating in a short wake condition and a longer sleep condition, were trained on the sequential finger-tapping task in the evening and were tested (1) after wake intervals of either 30 min or 4 h and (2) after a night of sleep that ensued either 30 min or 4 h after training. The results show an early boost in performance 30 min after training and a subsequent decay across the 4 h wake interval. When sleep followed 30 min after training, post-sleep performance was stabilized at the early boost level. Sleep at 4 h after training restored performance to the early boost level, such that, 12 h after training, performance was comparable regardless of whether sleep occurred 30 min or 4 h after training. These findings indicate that sleep does not enhance but rather stabilizes motor sequence performance without producing additional gains.

Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask

Abstract: Health care researchers have recently developed several home-based sleep monitoring systems and mobile applications that support healthy sleep by monitoring a user’s sleep environment, activities, or sleep quality This study describes the design and evaluation of an electrooculogram (EOG)-based system for an automatic sleep monitoring Compared with polysomnogram or electroencephalogram recordings, EOG has the advantage of easy placement and can be operated by the users themselves We also design an intelligent eye mask that is user friendly for measuring sleep stage and quality Two user experiments were carried out to demonstrate that the proposed system produces valid measurements of sleep stage and sleep quality and has good usability and reliability while not disturbing sleep significantly These findings suggest that our system can also be applied to long-term sleep monitoring or sleep environment control to improve the user’s sleep quality and make sleep more comfortable

Adjusting alarms based on sleep onset latency

Abstract: In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

Sleep stage classification by body movement index and respiratory interval indices using multiple radar sensors

Abstract: Disturbed sleep has become more common in recent years. To increase the quality of sleep, undergoing sleep observation has gained interest as an attempt to resolve possible problems. In this paper, we evaluate a non-restrictive and non-contact method for classifying real-time sleep stages and report on its potential applications. The proposed system measures body movements and respiratory signals of a sleeping person using a multiple 24-GHz microwave radar placed beneath the mattress. We determined a body-movement index to identify wake and sleep periods, and fluctuation indices of respiratory intervals to identify sleep stages. For identifying wake and sleep periods, the rate agreement between the body-movement index and the reference result using the RK p < 0.001). Although the degree that the 5-min fractal dimension contributed—another fluctuation index—was not as high as expected, its difference between REM and DEEP sleep was significant (p < 0.05). We applied a linear discriminant function to classify wake or sleep periods and to estimate the three sleep stages. The accuracy was 79.3% for classification and 71.9% for estimation. This is a novel system for measuring body movements and body-surface movements that are induced by respiration and for measuring high sensitivity pulse waves using multiple radar signals. This method simplifies measurement of sleep stages and may be employed at nursing care facilities or by the general public to increase sleep quality.

System and method for providing sleep recommendations using earbuds with biometric sensors

Abstract: Systems and methods for providing a sleep recommendation include providing a sleep recommendation using an earphone with a biometric sensor. The system includes a preferred sleep determination module that determines a preferred sleep duration. The system also includes a sleep debt module that creates and updates a sleep debt based on the preferred sleep duration and an actual sleep duration. In addition, the system includes a sleep recommendation module that provides a recommended sleep duration based on the sleep debt.

Communication device and communication method

Abstract: PROBLEM TO BE SOLVED: To provide a communication device capable of identifying an on-vehicle control unit may be a cause of battery exhaustion or the like out of a plurality of on-vehicle control units connected to a vehicle network, and further to provide a communication method.SOLUTION: A gateway ECU 50 comprises: a battery voltage acquisition section 51 acquiring information of a battery voltage of a battery 30 supplying electric power to each of ECU 11, 12, 21 through 23, 31 and 32; and a sleep propriety acquisition section 52 acquiring information indicating whether or not each of the ECU can be shifted to a sleep mode from each of the ECU. Further, a timer control section 54 of the gateway ECU 50 starts time counting of a timer serving as an index for identifying the ECU being a factor for stopping the ECU from being shifted to the sleep mode when the sleep propriety acquisition section 52 acquires information to the effect that the ECU cannot be shifted to the sleep mode under conditions that the battery voltage acquired by the battery voltage acquisition section 51 is less than a threshold voltage.

Time-dependent sleep stage transition model based on heart rate variability

Abstract: A new model is proposed to automatically classify sleep stages using heart rate variability (HRV). The generative model, based on the characteristics that the distribution and the transition probabilities of sleep stages depend on the elapsed time from the beginning of sleep, infers the sleep stage with a Gibbs sampler. Experiments were conducted using a public data set consisting of 45 healthy subjects and the model's classification accuracy was evaluated for three sleep stages: wake state, rapid eye movement (REM) sleep, and non-REM sleep. Experimental results demonstrated that the model provides more accurate sleep stage classification than conventional (naive Bayes and Support Vector Machine) models that do not take the above characteristics into account. Our study contributes to improve the quality of sleep monitoring in the daily life using easy-to-wear HRV sensors.

Baby sleep monitor

Abstract: A sleep monitor for monitoring baby sleep uses sleep state classification based on heartbeat feature respiration features. The sleep monitor automatically retrains the classification during use of the sleep monitor. Training examples for use in this training process are generated automatically by detecting time instants whereat the baby in the bed is in a wake state, based on signals from the at least one of a sound feature detector a movement feature detector ( 112 ) and an open eye detector ( 114 ). The retraining may comprise using time sequence from the end of detection of wake states to assign a class to heartbeat feature and/or respiration feature values during that time sequence for the training process. In an embodiment, the retraining comprises clustering detected heartbeat feature and/or respiration feature values detected outside the detected wake states.

Sleep stage determining method and sleep stage determining system

Abstract: An embodiment of the invention provides a sleep stage determining method and a sleep stage determining system. The sleep stage determining method includes: determining a first electrocardio characteristic parameter of an electrocardio signal, and determining whether a sleeper is in a waking stage or an REM (rapid eye movements) sleep stage or not within a time segment by means of judging whether the first electrocardio characteristic parameter satisfies a characteristic of the waking stage or the REM sleep stage or not within the time segment. Determination of a macro sleep structure is achieved by means of distinguishing the waking stage, the REM sleep stage or other similar sleep stages. Sleep stage determination is achieved by means of rule judgment, and accordingly, accuracy of sleep stage determination is guaranteed. Moreover, a sleep stage classifier can be used cooperatively to achieve comprehensive judgment on the sleep stages and can be used for classifying other kinds of sleep stages into a light sleep stage and a deep sleep stage so as to achieve a micro sleep structure, and accordingly, accuracy of sleep stage judgment is further improved.

System to monitor and assist individual's sleep

Abstract: A sleep assist system to monitor and assist the user's sleep comprises a bedside device positioned near the user's bed, the bedside device comprising a loudspeaker and a light source and optionally a microphone, a light sensor, a temperature sensor, a control unit, an air quality sensor, a display unit, a user interface. The sleep assist system further comprises a first sensing unit positioned in the user's bed and comprising one or more sensors adapted to sense at least pressure and/or changes in pressure exerted by the user lying in the bed. The system monitors the user's sleep, assesses the user's sleep cycles and the phase of sleep cycle, and provides the user with at least one light and sound program, the light and sound program being based on the assessment of the user's sleep cycles and the phase of sleep cycle.

Method and system for non-contact sleep monitoring

Abstract: The invention provides a method and a system for non-contact sleep monitoring The method comprises the following steps: using an infrared monitoring camera to acquire video images of a head region and abdomen and thorax regions of a human body, and using a somatosensory device to acquire attitude information of the human body; acquiring sleep information of the human body; and comparing the sleep information of the human body with preset standard sleep information, and analyzing health degree of human body sleep quality The method is based on an image processing technology, and acquires sleep signals of a human body by a non-contact method The method maintains natural sleep state of a user to the greatest extent, and sleep state information with physiological significance is extracted through a video image processing technology The system is low in device cost, and provides convenience for daily use of a family

Sleep measurement computer system

Abstract: A system and method that will serve for measuring the sleep of at least one user. The system will provide the measurement by communicating sleep parameters of at least one user to a mobile third party client application 170 from a sensor device 20. The mobile third party client application 170 is capable to automatically deduce the starting time of sleep without the user indicating the start of sleep. The system is configured to measure the sleep of more than one user by detecting the 10 presence of more than one user in the bed using a matrix of sensors 710.

Sleep monitoring system and method

Abstract: An improved method and system for monitoring a user's sleep is provided. The method includes monitoring, using one or more sensors of a bed, biological signal data of a user; determining, by a processor, a sleep phase of the user according to the biological signal data; and providing, on a data interface, a sleep cycle report, determined at least partially according to the sleep phase of the user.

Method and system for monitoring sleep quality based on pulse wave data

Abstract: The invention is suitable for the field of health monitoring technologies and provides a method and a system for monitoring sleep quality based on pulse wave data. The method comprises the following steps of monitoring the pulse wave data in real time through a pulse wave infrared detection sensor; monitoring action data of a monitored person in a sleep process through an action sensor, wherein the action data comprises large actions and small actions; obtaining the sleep state, the starting and ending time of each sleep state and the time bucket of each sleep state of the monitored person according to the pulse wave data, the action data and the detection time, wherein the sleep state comprises an awakening period, a light sleep state, a deep sleep state, a dreaming interval and a micro-arousal period; obtaining the sleep quality of the monitored person through combining a Chinese medical sleep health maintenance theory according to the starting and ending time of each sleep state and the time bucket of each sleep state. The method and the system have the advantages of high collection efficiency and accurate sleep quality analysis.

Method and apparatus for alarm service using context awareness in portable terminal

Abstract: A method and apparatus for an alarm service using context awareness in a portable terminal are provided. A control method for preventing the disruption of sleep includes sensing if a user is in a sleep state and, when the user is in the sleep state, adjusting a volume level to a preset volume level, and adjusting screen brightness to a preset screen brightness when the user is in the sleep state.

Voice frequency sleep assisting device based on brain wave signals

Abstract: The invention discloses a voice frequency sleep assisting device based on brain wave signals. The device utilizes a digital signal processor (DSP) as the core to achieve real-time analysis of brain wave signals, pulses, eye movement, head movement and the like. The fact that a user is in the sleep state is defined according to a signal analyzing comprehensive result, and different voice frequencies in a voice frequency storage are selected according to the sleep degree to help the user to enter the sleep. After the user enters the sleep, the device enters the standby mode, and the voice frequencies are closed automatically. The device is good in sleep assisting effect and has good adaptability on a human body, a measurement sensor and an electrode are installed in a head belt which is bound to the forehead of the user and does not affect the sleep gesture of the user. The system is operated full automatically without any external adjusting command. The voice frequency output adopts a paster headset or a bone transfer module sensor which is arranged in the head belt or a pillow and is not arranged in the auditory meatus, and no long-term discomfort is generated.

Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling

Abstract: Following healthy lifestyle is a key for active living. Regular exercise, controlled diet and sound sleep play an invisible role on the well being and independent living of the people. Sleep being the most durative activities of daily living (ADL) has a major synergistic influence on people's mental, physical and cognitive health. Understanding the sleep behavior longitudinally and its underpinning clausal relationships with physiological signals and contexts (such as eye or body movement etc.) horizontally responsible for a sound or disruptive sleep pattern help provide meaningful information for promoting healthy lifestyle and designing appropriate intervention strategy. In this paper we propose to detect the microscopic states of the sleep which fundamentally constitute the components of a good or bad sleeping behavior and help shape the formative assessment of sleep quality. We initially investigate several classification techniques to identify and correlate the relationship of microscopic sleep states with the overall sleep behavior. Subsequently we propose an online algorithm based on change point detection to better process and classify the microscopic sleep states and then test a lightweight version of this algorithm for real time sleep monitoring activity recognition and assessment at scale. For a larger deployment of our proposed model across a community of individuals we propose an active learning based methodology by reducing the effort of ground truth data collection. We evaluate the performance of our proposed algorithms on real data traces, and demonstrate the efficacy of our models for detecting and assessing fine-grained sleep states beyond an individual.

Sleep analysis method and device based on environment monitoring

Abstract: The invention relates to a sleep analysis method and device based on environment monitoring. The movable and wearable sleep analysis device is adopted in the sleep analysis method for conducting sleep analysis based on environment monitoring. The movable and wearable sleep analysis device comprises a core processor, a three-axis gravity acceleration sensor, an environment monitoring module, a display module and a sleep monitoring module based on the body temperature and the heart rate. The core processor monitors the human body sleep stage according to body temperature and heart rate information sent by the sleep monitoring module, judges the sleep quality of the human body, analyzes the influences caused by the change of environment parameters on the sleep quality according to environment parameter information sent by the environment monitoring module, finds the environment condition suitable for the sleep of a user most and improves the sleep quality of the user. The problems that an existing sleep monitoring device can not analyze the sleep quality according to the change of environment parameters or provide the optimal environment parameters affecting the sleep quality are solved.

Improving sleep after open heart surgery–Effectiveness of nursing interventions

Abstract: Background and Objective : Cardiac surgical patients experience sleep problems in the early post-operative period and after hospital discharged. Restorative sleep is important to be able to handle the challenges of rehabilitation, but often remains untreated. Pharmacological treatment has been preferred, but studies conclude a longer lasting effect of cognitive behavioural therapy (CBT). Few clinical trials have been conducted on nurse led sleep promoting interventions during hospitalization. The hypothesis of this study is that systematic training and education in sleep, sleep anamneses and sleep hygiene enhances nurses ’ awareness on sleep problems, and as a result makes nurses able to propose appropriate interventions to improve patients’ sleep during hospitalization, and after discharged. The aim is to examine the effect on patients’ self-reported sleep quality. Methods : The study design is a controlled intervention study. Patients in the control group received usual care. Patients in the intervention group received nursing focused on improving sleep by use of sleep-anamneses and sleep hygienic principles. Patients ’ sleep-quality was measured preoperatively, and one and two month post-operatively by use of PSQI-questionnaire and sleep diaries. Results : There was no significant effect of the intervention, though there were several signs that had some effect after two months in terms of global PSQI, total sleep time, sleep efficiency, sleep medication and sleep quality. Conclusions : Systematic education and training of nurses in sleep, sleep anamneses and sleep hygienic principles has some effect on patients self-reported sleep quality two months after heart surgery.

Sleep regulation and management system

Abstract: The invention discloses a sleep regulation and management system which comprises a main control unit, an induction driving module, a communication module, a storage module and a real-time clock module, wherein the main control unit is respectively connected with the induction driving module, the communication module, the storage module and the real-time clock module and used for controlling the induction driving module, the communication module, the storage module and the real-time clock module; the periphery of the main control unit is also provided with a reminding component, an expansion interface, a user input component and a power source The sleep regulation and management system is capable of providing an alternative scheme for an existing awakening mode, preventing damages on health of users, solving the defects existing in sleep management products or patents, realizing sleep data acquisition with high practicability and precision and low cost, providing a practical scheme which summarizes and includes, but not limited to sleep states of non-organic insomniacs and thus improving the sleep quality of the users

Method and system to optimize lights and sounds for sleep

Abstract: Method for optimizing light and sound programs for a falling asleep phase (ST1) and for an awakening phase (ST2) of a first user (U01), in a system comprising, for the first user and other users, a bedside device (1), a bio parameter sensor (2,3), a smartphone (8), and additionally for all users a central server (5), with: /a1/ collecting, with regard to the first user U01, sleep data and sleep context data, said sleep data comprising at least light and sound program played for the falling asleep phase and for the awakening phase, bio parameters and sleep patterns sequence deduced therefrom, said sleep context data comprising at least previous daytime activity such as, sending this data to the central server (5), /a2/ repeat /a1/ for other users, /b1/ building a user-specific model of each user sleep behavior, /c/ comparing user-specific models to define groups of similar users, each group of users being allocated with a group meta-model with decision rules and preferred playlist of sound tracks, /d/ sending the group meta-model from the server to the bedside device or to the smartphone of the first user U01, /e/ displaying to the first user, using the group meta-model and in function of the time to go to sleep, a recommended list of light and sound programs or a particular light and sound program, namely a single choice, for the upcoming falling asleep phase and/or the next upcoming wakeup phase

Intelligent pillow and sleep quality monitoring method

Abstract: The invention provides an intelligent pillow and a sleep quality monitoring method. The intelligent pillow and the sleep quality monitoring method comprise a pillow main body, a hardware platform and a software platform; the hardware platform comprises a micro-controller, a temperature sensor module, a blue tooth module, a sound sensor module and a pressure sensor module. By means of the intelligent pillow and the sleep quality monitoring method, multiple sensors are arranged, various information such as pressure, head subsection and snore which are produced in the process of sleep of users is accurately acquired, the sleep information can be displayed on a mobile device visually and clearly through the software platform, the sleep information is analyzed, and suggestions are provided, so that the users can acquire and understand personal sleep information. Therefore, the sleep habits of the users can be changed, and the sleep quality and health of the users are improved.

Inferring user sleep patterns

Abstract: Methods, computer systems, and computer storage media are provided for inferring sleep-related aspects for a user based, in part, on sensor data reflecting user activity detected by one or more sensors. In an embodiment, a user sleep model is trained using a dataset that includes previously-sensed data, descriptive information associated with the previously-sensed data, and/or interpretive data extracted from the previously-sensed data describing circumstances surrounding users when the data was acquired. In an embodiment, services providing time-sensitive recommendations personalized for a user's sleeping pattern using the inferred sleep-related aspects.