Early alert for sleep deprivation using mobile sensor data fusion. (September 2022)

• Record Type: Journal Article
• Authors: Sah, Dinesh Kumar; ravindra, Chaya; Cengiz, Korhan; Alshehri, Yasser; Alnazzawi, Noha; Ivković, Nikola
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Sleep deprivation -- Vibrometer -- Healthcare -- Sensor data fusion -- Deep neural network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108228
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In recent years, with the widespread use of digital technology and intelligent gadgets, an enormous amount of data are being generated at an alarmingly rapid rate. The quickly growing technologies and data generated by mobile and Internet of Things devices are tremendous. These data can be used to achieve sleep deprivation in users using machine learning. Multi-modal sensors and technology to detect activity, sleep, and circadian rhythms have become increasingly widespread. For the first time, large-scale sleep tracking is conceivable. In addition to large-scale epidemiological research, multi-sensor data can be used for wellness applications such as sleep coaching for chronic disease patients. Individuals, health, and research must overcome several fundamental obstacles before new technologies may ultimately benefit. We examine existing sleep-monitoring technology from data gathering through mobile data sensors to machine learning mode. We also analyze the current and emerging sensing technologies. Graphical abstract: Highlights: An early alert of sleep deprivation using mobile sensor data to monitor sleep deprivation. The Deep neural network model is developed to accumulate sensor fusion. The categorization of sleep situations has been shown based on user data and learning. Our model can provide monitoring throughout the night.