Sleep (system call)

About: Sleep (system call) is a research topic. Over the lifetime, 2633 publications have been published within this topic receiving 27806 citations. The topic is also known as: Sleep() & sleep().

Sleep temperature control method and device

Abstract: The invention discloses a sleep temperature control method. The sleep temperature control method comprises the following steps: detecting current ambient temperature and a user sleep state, wherein the steep state comprises a deep sleep state, a light sleep state and a wake state; statistically collecting temperature detected at each preset time point to form a sleep temperature curve; determining whether to set the current statistically-collected sleep temperature curve as an optimum sleep temperature curve or not according to the time length of the deep sleep state; outputting the optimum sleep temperature curve into an air conditioner to control the air conditioner to adjust the current ambient temperature. The invention additionally discloses a sleep temperature control device. The sleep temperature control method and the sleep temperature control device improve the flexibility and the practicability of sleep temperature curve setting.

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.

Method and apparatus for implementing a high-precision interval timer utilizing multiple oscillators including a non-optimal oscillator

Abstract: The present invention is a novel method and apparatus for implementing a high-precision timer utilizing a non-optimal oscillator and a high-speed oscillator wherein only one oscillator is enabled at a given moment in time. The high-precision timer method and apparatus comprises a timer and an error-correction technique. In one embodiment, the timer of the present invention is constructed from a high-speed oscillator and a low-speed non-optimal oscillator. The timer operates from the high-speed oscillator during on-the-air modes of operation and from the low-speed non-optimal oscillator during sleep modes of operation. The present inventive method corrects errors that are introduced by the non-optimal oscillator and a swallow counter. The errors are corrected using an error-correction technique having two steps: an error-determination step and an error-correction step. In the preferred embodiment of the error-determination step, a total error for a time interval is determined by performing the following steps: (1) calculating an individual error that occurs at each pulse; (2) multiplying the individual error by the number of pulses occurring during the time interval; and (3) adjusting for a non-optimal counter. Once an error has been determined, the error-correction step adjusts a clock counter accordingly. Depending upon the error-correction technique used, the error-correction step can correct the total error at one of several locations within a timer counter chain that is used to practice the present invention. The implementation of the present invention allows a straightforward realization of multiple timers.

A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging

Abstract: Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient’s progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.

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.