Advances in Sensing Technologies for Sleep Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 30 May 2025 | Viewed by 127
Special Issue Editors
Interests: digital health; machine learning and AI; sensing technologies in healthcare; human-computer interaction in healthcare
Special Issues, Collections and Topics in MDPI journals
Interests: wearable devices; distributed computing; energy harvesting; battery-free sensors; privacy-preserving AI; edge ML; precision health
Interests: sensing; machine learning; UX research; hardware/software development; product/project management; the evaluation of interactive technologies
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sleep is a biological necessity and crucial to maintaining healthy mental and physical functioning. The increasing interest in monitoring and improving sleep behaviours has spurred the development of sensing technologies and devices in recent years. These devices can provide objective measures of sleep, which are less biassed than self-reported measures and are more economical and less labour-intensive to obtain compared to the current gold standard, polysomnography.
Recent advances in sensing and digital health technologies and data science facilitate longitudinal monitoring of sleep and big data collection and analysis, providing great opportunities to explore the short- and long-term impact of sleep changes on health and wellbeing.
This Special Issue of the Sensors aims to bring together interdisciplinary research on emerging sensing technologies and data-driven approaches (including but not limited to sensor technologies, data science, AI, big data analytics, telehealth, and remote monitoring) to accurately monitor sleep behaviours.
Topics of interest include:
- Sensing technologies and AI algorithms for sleep monitoring;
- Novel data-driven approaches for sleep behaviour assessment;
- Digital health and remote monitoring frameworks that support effective implementation and deployment of data- and technology-driven approaches for sleep monitoring;
- Case studies on co-design, feasibility, usability, acceptance, and performance of sensing technology and data-driven tools for sleep monitoring and assessment.
Original research and comprehensive review articles will be considered. Submitted manuscripts should not have been previously published or currently under review by other journals, conferences, symposiums, or workshops.
Dr. Mahnoosh Kholghi
Dr. Moid Sandhu
Dr. David Silvera-Tawil
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- sleep monitoring
- sensing technology
- AI
- IoT
- data science
- remote monitoring
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