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().

Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions

Abstract: Consumer sleep tracking technologies offer an unobtrusive and cost-efficient way to monitor sleep in free-living conditions. Technological advances in hardware and software have significantly improved the functionality of the new gadgets that recently appeared in the market. However, whether the latest gadgets can provide valid measurements on overall sleep parameters and sleep structure such as deep and REM sleep has not been examined. In this study, we aimed to investigate the validity of the latest consumer sleep tracking devices including an activity wristband Fitbit Charge 2 and a wearable EEG-based eye mask Neuroon in comparison to a medical sleep monitor. First, we confirmed that Fitbit Charge 2 can automatically detect the onset and offset of sleep with reasonable accuracy. Second, analysis found that both consumer devices produced comparable results in measuring total sleep duration and sleep efficiency compared to the medical device. In addition, Fitbit accurately measured the number of awakenings, while Neuroon with good signal quality had satisfactory performance on total awake time and sleep onset latency. However, measuring sleep structure including light, deep, and REM sleep remains to be challenging for both consumer devices. Third, greater discrepancies were observed between Neuroon and the medical device in nights with more disrupted sleep and when the signal quality was poor, but no trend was observed in Fitbit Charge 2. This study suggests that current consumer sleep tracking technologies may be immature for diagnosing sleep disorders, but they are reasonably satisfactory for general purpose and non-clinical use.

A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment

Abstract: Sleep apnea, which is a common sleep disorder characterized by the repetitive cessation of breathing during sleep, can result in various diseases, including headaches, hypertension, stroke and cardiac arrest, as well as produce severe consequences such as impaired concentration and traffic accidents. A traditional diagnosis method of sleep apnea is polysomnography, which can only be conducted in sleep center with specialized personals, thus is expensive and inconvenient. Moreover, it is only used for understanding the conditions, without treatment function. Some other methods or devices have been developed to alleviate sleep apnea, such as continuous positive airway pressure (CPAP) and intraoral mandibular advancement device and surgery. However, they only provide a treatment method without detection or monitoring function. There is no existing device which can provide both apnea detection and treatment functionality. In this paper, we propose and implement a smart phone-based auto-adjustable pillow system, which enables both sleep apnea detection and treatment. Sleep apnea events can be detected in real-time using the blood oxygen sensor, accordingly, the height and shape of the pillow can be automatically adjusted to terminate the sleep apnea event. On the other hand, after the adjustment, the sensor can continuously monitor the blood oxygen signal to evaluate the effectiveness of the pillow adjustment and to help in selecting a suitable adjustment scheme. Therefore, a real-time feedback control system is formed. Besides, compared with existing diagnosis or treatment devices, our system is non-invasive, inexpensive and portable, which can be used at home or during traveling. In this paper, a real-time sleep apnea detection and classification algorithm is proposed to decide whether the pillow should be adjusted or not. We also design a real-time feedback pillow adjustment algorithm, to decide when and how to adjust the pillow and how to evaluate the effectiveness of the adjustment. We conducted experiments on 40 patients, which demonstrate that using our novel smart pillow system, both the sleep apnea duration and the number of sleep apnea events are dramatically reduced by more than 50%.

Retriggerable sleep timer with user-prompting mode operation

Abstract: A sleep timer function for consumer electronics equipment automatically turns off the equipment after a predetermined time interval. During the final two minutes of the sleep time interval, the sleep timer circuitry gradually decreases the volume or provides a visual indication that automatic turn off is imminent. The reception of any modulated infrared (IR) signal, transmitted from any source, during the timer period is interpreted by the sleep timer function controller as an indication that the use is still awake. In response, the controller resets the count of the sleep timer to the maximum period, thus deferring the automatic turn off of the equipment.

Compare and exchange operation using sleep-wakeup mechanism

Abstract: A method, apparatus, and system are provided for performing compare and exchange operations using a sleep-wakeup mechanism. According to one embodiment, an instruction at a processor is executed to help acquire a lock on behalf of the processor. If the lock is unavailable to be acquired by the processor, the instruction is put to sleep until an event has occurred.

Insomnia assessment and treatment device and method

Abstract: A method of automated sleep behavior modification for insomnia and other sleep problems using a computerized treatment system including: determining a baseline sleep pattern of a user and storing in the system data indicative of the baseline sleep pattern; generating a sleep behavior regimen based on the stored sleep pattern data and a behavior modification algorithm, wherein the algorithm generates sleep inducement behavior prompts; prompting the user to perform a predetermined sleep inducement behavior at a time determined by the regimen; tracking the sleep behavior of the user and storing data indicative of the tracked sleep behavior; modifying the regimen based on the tracked sleep behavior, and prompting the user to perform the predetermined sleep inducement behavior at a time determined by the modified regimen.