Given these trends and the implications of sleep for health and well-being, better characterisation of sleep characteristics represents a public health priority 9, 10, 11. Global trends in sleep suggest a decrease on average sleep duration 5, 6, 7, 8. Even transient changes in sleep patterns, such as acute sleep deprivation, can impair judgement and cognitive performance, whilst long-term aberrations have been linked to disease development 3, 4.
Whilst not all of sleep’s functions are fully understood, it is known to restore energy, promote healing, interact with the immune system and impact upon both brain function and behaviour 1, 2. Sleep is a crucial biological process, and has long been recognised as an essential determinant of human health and performance. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human–computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation.
#Smartsleep research articles full
However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions.
These developments make accurate sleep monitoring at scale a possibility for the first time. In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms.
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