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().

Time of day accounts for overnight improvement in sequence learning.

Abstract: The theory that certain skills improve with a night of sleep has received considerable interest in recent years. However, because sleep typically occurs at the same time of day in humans, it is difficult to separate the effects of sleep from those of time of day. By using a version of the Serial Response Time Task, we assessed the role of sleep in implicit sequence learning while controlling for possible time-of-day effects. We replicated the apparent benefit of sleep on human participants. However, our data show that sleep does not affect implicit sequence learning; rather, time of day affects the ability of participants to express what they have learned.

Will you have a good sleep tonight?: sleep quality prediction with mobile phone

Abstract: Understanding the relationship between sleep and daily life can provide insights into a healthy life style since the sleep quality is one of the most important indicators of people's health status. This paper studies the extent to which a person's sleep quality can be predicted by his/her daily context information. A combination of the machine learning technology and medical knowledge is used to study the relation between context and sleep quality, so that sleep quality can be predicted in real time according to the relation.We propose a novel sleep quality predicting framework from user context data, without requiring users to wear special devices. We develop a data collecting and analyzing prototype system called SleepMiner, which uses on-phone data such as mobile sensor data and communication data to extract human contexts. Then the relationship between context data and sleep quality is analyzed and a learning model based on factor graph model is proposed to predict sleep quality. From experimental results we demonstrate that it is possible to accurately infer sleep quality (around 78%) from user context information. A set of solutions are proposed to address the practical problems of Android phone in data collection, making SleepMiner work with minimal impact on the phone's resources. We finally carry out experiments to evaluate our design in effectiveness and efficiency.

Unobtrusive monitoring of sleep patterns

Abstract: Disrupted sleep is a common problem in the elderly, due to age-related changes in health, lifestyle, and the physiological aspects of sleep. Severe sleep disturbances lead to impaired functioning, reduced quality of life, and increased health care costs. Therefore, monitoring of sleep patterns in the elderly is important. However, current methods for monitoring sleep are inadequate. In this paper, we use load cells for unobtrusive continuous monitoring of sleep patterns. Load cells are placed under a bed, and sleep characteristics such as bedtime, wake up time, and number and duration of naps are inferred from the load cell data. Using information from each load cell individually, we can also identify a person's position in bed, and, consequently, detect position shifts. We describe the algorithms for computation of the sleep characteristics and for assessment of the person's position in bed. We conclude by discussing the limitations in our approach, and the work we intend to pursue in the future.

Monitoring sleep stages to determine optimal arousal times and to alert an individual to negative states of wakefulness

Abstract: A system and method for monitoring sleep stages to determine optimal arousal times and to alert an individual to negative states of wakefulness. In an embodiment, a device receives pressure data from at least one pressure sensor, where the pressure sensor is associated with furniture used for an individual to sleep. The device uses the pressure data to determine a sleep stage for the individual. The sleep stage is used to determine whether it is an optimal arousal time for the individual. The device sends an indication to not wake the individual if it is not the optimal arousal time for the individual. Other embodiments are described and claimed.

Apparatus and method for the treatment of sleep apnea, arrhythmia, and partial epilepsy

Abstract: An apparatus to detect the onset of sleep apnea, and to provide an automated way to awaken the sleeping patient at the onset of sleep apnea. The apparatus may also contain a recording device or computer that captures blood oxygen levels and pulse rates throughout the period of sleep, and may contain computer programs, algorithms, subroutines or logic to determine the level of blood oxygen and pulse rate that indicates the onset of a sleep apnea event. The method of arousing the patient from sleep at the onset of a sleep apnea event will decrease or eliminate the occurrence of sleep apnea, arrhythmia, and partial epilepsy over time.