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().

Chapter 10 – Sleep in Animals: A State of Adaptive Inactivity

Abstract: • In most adult animals sleep is incompatible with mating and feeding. Many researchers view animals as being highly vulnerable to predation during sleep. Why do animals devote from 2 to 20 hours of the day to sleep, in what appears to be a nonproductive state? Why has evolution preserved this state?The presence of sleep in nearly all animals and the enormous variation in sleep time across species are best explained as adaptations to ecologic and energy demands. • Sleep is not a maladaptive state that needs to be explained by undiscovered functions (which nevertheless undoubtedly exist). Rather, the major function of sleep is to increase behavioral efficiency. Greater waking activity does not necessarily lead to increased numbers of viable offspring and, hence, genetic success. Rather, genetic success is closely linked to the efficient use of resources and to the avoidance of risk. Thus, inactivity can reduce predation and injury. It also reduces brain and body energy consumption. As often stated, energy conservation is not a sufficient explanation for sleep, because the energy saved in a night’s sleep in humans is only equivalent to that contained in a slice of bread. In the wild, however, most animals are hungry and are seeking food most of the time they are awake. If ample food is available, the population of a species quickly expands until faced again with food scarcity, a phenomenon that is illustrated by the great increase in the human population. • The ability of sleep to conserve energy when food is scarce constitutes a major survival benefit even if only a small amount of energy is saved. Conversely, if food is available but is time-consuming to acquire, it is highly advantageous for animals to be able to reduce sleep time without behavioral impairment. Similarly, it is highly advantageous to reduce or eliminate sleep to allow migration and to respond to certain other needs. Several examples of extended periods of elimination or substantial sleep reduction without rebound have recently been documented and appear to be strongly linked to species success. • Many researchers have assumed that predation risk is increased during sleep—that more animals are killed per hour during sleep than during waking. However there is scant evidence to support this contention. Most animals seek safe sleeping sites, often underground, in trees or in groups that provide communal protection. Those large herbivores that cannot find safe sleeping sites appear to require smaller amounts of sleep and to sleep less deeply. Large animals that are not at risk for predation, such as big cats and bears, can sleep for long periods, often in unprotected sites, and appear to sleep deeply. Chapter Highlights

Method for controlling loudspeaker volume and terminal device

Abstract: The present invention discloses a method for controlling loudspeaker volume and a terminal device. The method comprises: obtaining user's sleep data; analyzing the sleep data, and determining the user's sleep state changing; and sending control signals to a multimedia device based on the sleep state changing to control the loudspeaker volume of the multimedia device. The method for controlling loudspeaker volume and the terminal device are able to control the loudspeaker volume of the multimedia device to be turn up and turn down the volume based on the user's sleep state changing, allow the loudspeaker volume of the multimedia device to perform autonomous regulation according to the user's sleep state so as to improve the user's adaptability for the volume decibel, allow the user to sleep safely, improve the user's sleep quality in the sleep process, and allow the user who is awake just now to not feel harsh loudspeaker volume.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Abstract: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

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.

Method and device for optimizing electricity consumption of application program and electronic device

Abstract: Embodiments of the invention disclose a method and a device for optimizing electricity consumption of an application program and an electronic device. The method comprises the following steps: after arriving at a preset sleep monitoring time, obtaining the scene information of the electronic device for preset times according to a preset time period, and determining that the electronic device is in a sleep scene according to the obtained scene information; obtaining a pre-stored sleep operation application program white list in the electronic device, extracting application programs beyond the sleep operation application program white list in the electronic device, and optimizing the electricity consumption according to a preset electricity consumption optimization processing strategy. The method disclosed by the invention can be used for prolonging the endurance time of the electronic device and improving the electricity consumption optimization efficiency of the electronic device.