Evolution of bed-based sensor technology in unobtrusive sleep monitoring: a review
With the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in v...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
1 OCTOBER 2024
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| In: |
IEEE sensors journal
Year: 2024, Volume: 24, Issue: 19, Pages: 29545-29563 |
| ISSN: | 1558-1748 |
| DOI: | 10.1109/JSEN.2024.3439743 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/JSEN.2024.3439743 Verlag, lizenzpflichtig, Volltext: https://ieeexplore.ieee.org/document/10640370 
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| Author Notes: | Mostafa Haghi , Member, IEEE, Maksym Gaiduk , Member, IEEE, Marvin Stoffers, Nima TaheriNejad , Member, IEEE, Thomas Penzel , Fellow, IEEE, Natividad Martínez Madrid , Senior Member, IEEE, and Ralf Seepold , Senior Member, IEEE |
| Summary: | With the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in various settings. We conducted a retrospective literature review, summarizing the state-of-the-art research over the past decade on non-contact bed-based sensor technology in sleep monitoring. We developed a three-category terminology: unobtrusive sensor technology, application, and subject. A total of 263 unique articles were acquired from three databases and screened for relevance, resulting in 21 papers selected for in-depth analysis. The findings revealed eight types of sensors: six mechanical sensors (pressure, accelerometer, piezoelectric, load cell, electromechanical film (EMFI), and hydraulic) and two FOSs (fiber Bragg grating and microbend FOS) that are integrated with or positioned under the bed at three levels of unobtrusiveness. We identified 15 parameters, with heart rate (HR) (14) and respiratory rate (RR) (13) being the most frequently measured. These parameters are generally categorized into three applications: disease-related diagnosis (18), general sleep analysis (9), and general well-being (11). The results indicated that sleep apnea (5) and insomnia (2) were the most frequently detected sleep disorders. Additionally, 59.1% (13) of the systems were tested in a lab environment, with only one undergoing clinical trials. In summary, there is a clear lack of convincing proof of the systems’ effectiveness in continuous in-home sleep monitoring. |
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| Item Description: | Gesehen am 10.04.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1558-1748 |
| DOI: | 10.1109/JSEN.2024.3439743 |