Recent Advancement in Sleep Technologies: A Literature Review on Clinical Standards, Sensors, Apps, and AI Methods

• Published in: IEEE access Vol. 10; pp. 104737 - 104756
• Main Authors: Cay, Gozde, Ravichandran, Vignesh, Sadhu, Shehjar, Zisk, Alyssa H., Salisbury, Amy L., Solanki, Dhaval, Mankodiya, Kunal
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI for sleep; Applications programs; Artificial intelligence; Behavioral sciences; Biomedical imaging; Biomedical monitoring; Electric contacts; Electroencephalography; Literature reviews; Machine learning; Mobile computing; Monitoring; Muscles; Oscillators; Rapid eye movement sleep; Scientific papers; Sensors; Sleep; sleep disorders; sleep interventions; sleep medicine; sleep monitoring; sleep sensors; sleep technology; State-of-the-art reviews
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3210518

This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including "sleep monitoring", "sleep monitoring technologies", "non-contact sleep monitoring", "mobile apps for sleep monitoring", "AI in sleep technologies", and "automated sleep staging." The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.