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().

System for sleep apnea monitoring

Abstract: The present invention relates to a sleep apnea monitoring system for monitoring a sleep state of a user by using impedance tomographic image information on a bio-signal and a characteristic of a tissue with respect to a measured part of a user The present invention can be simply manufactured so as to measure the body during natural sleep at home, and it can be used for the diagnosis and treatment plan of sleep apnea by using information of the sleep state acquired through the same

Vehicle sleeps method and system

Abstract: The present invention provides a vehicle sleep method and system. The method comprises the following the steps that: the non-single wake-up sources of a vehicle are determined in advance; and when the state of each non-single wake-up source satisfies local sleep conditions, a vehicle body controller transmits a message on a CAN bus, wherein the message indicates that the other controllers are allowed to sleep; the CAN bus and the other controllers can enter sleep states under a premise that the vehicle body controller does not affect the work of the other controllers, and therefore, energy consumption can be reduced; and the vehicle body controller judges the vehicle body controller itself satisfies sleep conditions, and the CAN bus has enters a sleep state, the vehicle body controller enters a sleep state, and therefore, the safety of the vehicle can be effectively guaranteed.

Sleep insights from the finger tip: How photoplethysmography can help quantify sleep

Abstract: Sleep is essential for a healthy and productive life, yet its importance is largely overlooked, allowing populations to sleep less and to develop sleep disturbances. This trend results into an epidemic of poor quality and insufficient sleep that is turn jeopardizes health, performance, mood, memory, social relationships and productivity. A first step to overcome this epidemic relies on uncovering it at the individual and societal levels. The availability of different wearable devices that can track physiological signals represents a great opportunity to define and quantify the problem. Many such devices incorporate a photoplethysmography (PPG) sensor. This triggers interest in studies aiming to get insight into human sleep structure based on information obtained from PPG sensors. This study aimed to validate a new automated sleep analysis which is simply based on the inter-beat-interval series obtained from PPG, and that uses features of heart rate variability. The candidate algorithm was tested against gold standard scoring of whole night state-of-the-art sleep studies. The PPG-based sleep scoring performs very well in differentiating sleep stages, however, the sleep/wake separation is not sufficient and requires improvement. This last task is facilitated by the fact that the majority of devices with PPG capabilities, are equipped with accelerometers providing additional information for better separation. Combining accelerometers and PPG signals from wearable devices in a sleep analyser is likely to provide a reliable and accurate automated detection of sleep and wakefulness, including sleep macro- and micro-architecture.