Naslov
Early alert for sleep deprivation using mobile sensor data fusion

Autori
Sah, Dinesh Kumar ; Ravindra, Chaya ; Cengiz, Korhan ; Alshehri, Yasser ; Alnazzawi, Noha ; Ivković, Nikola

Izvornik
Computers & electrical engineering (0045-7906) 102 (2022); 108228, 11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Sleep deprivation ; Vibrometer ; Healthcare ; Sensor data fusion ; Deep neural network

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

POVEZANOST RADA