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Detection of melatonin-onset in real settings via wearable sensors and artificial intelligence : a pilot study
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Castaldo, Rossana, Chappell, M. J., Byrne, H., Innominato, Pasquale F., Hughes, S., Pescapè, A. and Pecchia, Leandro (2021) Detection of melatonin-onset in real settings via wearable sensors and artificial intelligence : a pilot study. Biomedical Signal Processing and Control, 65 . 102386. doi:10.1016/j.bspc.2020.102386 ISSN 1746-8094.
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WRAP-detection-melatonin-onset-real-settings-via-wearable-sensors-artificial-intelligence-Castaldo-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1252Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.bspc.2020.102386
Abstract
Circadian rhythms modulate physiological and behavioral processes of approximately 24-h periodicity. Alterations in the circadian timing system may lead to cardiovascular, metabolic or neurological diseases, cancers and sleep disorders, as well as to disruption of quality of life. Circadian rhythms can be tracked via laboratory tests measuring hormones in salivary, urinary or blood samples, which are collected in controlled environments. These tests are unsuitable for continuous monitoring in real-life, being expensive and time consuming, producing discrete information (i.e., few values per day) and requiring controlled environmental conditions (e.g., exposure to light can alter the samples). Thus, there is a need to develop non-invasive methods and tools to track circadian rhythms in real-life conditions.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Circadian rhythms, Wearable computers , Detectors, Smart materials , Heart rate monitoring , Machine learning | |||||||||
| Journal or Publication Title: | Biomedical Signal Processing and Control | |||||||||
| Publisher: | Elsevier BV | |||||||||
| ISSN: | 1746-8094 | |||||||||
| Official Date: | March 2021 | |||||||||
| Dates: |
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| Volume: | 65 | |||||||||
| Article Number: | 102386 | |||||||||
| DOI: | 10.1016/j.bspc.2020.102386 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 6 January 2021 | |||||||||
| Date of first compliant Open Access: | 8 January 2021 | |||||||||
| RIOXX Funder/Project Grant: |
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