Abstract
Sleep plays a vital role in a person’s health and well-being. Unfortunately, most people suffering from sleep disorders remain without diagnosis and treatment since the current sleep assessment systems are cumbersome and expensive. As a result, there is an increasing demand for cheaper and more affordable sleep monitoring systems in real-life environments. In this paper, we propose a novel non-intrusive system for sleep quality monitoring using a microbend fiber optic mat placed under the bed mattress. The sleep quality is assessed based on different parameters. Moreover, the sensor has been integrated into an existing Ambient Assisted Living framework to be validated in real scenarios. Three senior female residents participated in our study and the sleep data was collected over a one-month period in a home-living situation. The proposed system shows accurate and consistent results with a survey collected from each participant showing their sleep patterns and other in-home activities.
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Notes
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http://www.hdb.gov.sg Housing & Development Board is a Singaporean governmental organization responsible for public housing, on their website, HDB claims: “HDB flats are home to over \(80\%\) of Singapore’s resident population”.
References
Mihailidis, A., et al.: Zero effort technologies: considerations, challenges, and use in health, wellness, and rehabilitation. Synth. Lect. Assistive Rehabil. Health Preserv. Technol. 1(2), 1–94 (2011)
Koenig, S.M., et al.: Sleep and Sleep Assessment Technologies. Humana Press, Totowa (2008)
Bellmunt, J., et al.: Agile framework for rapid deployment in ambient assisted living environments. In: Proceedings of the 18th International Conference on Information Integration and Web-Based Applications and Services. iiWAS 2016, pp. 410–413. ACM, New York (2016)
Watanabe, T., Watanabe, K.: Noncontact method for sleep stage estimation. IEEE Trans. Biomed. Eng. 51(10), 1735–1748 (2004)
Kortelainen, J.M., et al.: Sleep staging based on signals acquired through bed sensor. IEEE Trans. Inf. Technol. Biomed. 14(3), 776–785 (2010)
Matar, G., et al.: Internet of Things in sleep monitoring: an application for posture recognition using supervised learning. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6, September 2016
Paalasmaa, J., et al.: Unobtrusive online monitoring of sleep at home. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3784–3788, August 2012
Rosales, L., et al.: Heart rate monitoring using hydraulic bed sensor ballistocardiogram1. J. Ambient Intell. Smart Environ. 9(2), 193–207 (2017)
Sadek, I., et al.: Sensor data quality processing for vital signs with opportunistic ambient sensing. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2484–2487, August 2016
Sadek, I., et al.: Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position. In: IEEE-EMBS International Conferences on Biomedical and Health Informatics, pp. 289–292, February 2017
Bellmunt, J., Tiberghien, T., Mokhtari, M., Aloulou, H., Endelin, R.: Technical challenges towards an AAL large scale deployment. In: Geissbühler, A., Demongeot, J., Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds.) ICOST 2015. LNCS, vol. 9102, pp. 3–14. Springer, Cham (2015). doi:10.1007/978-3-319-19312-0_1
Aloulou, H., Abdulrazak, B., Endelin, R., Bentes, J., Tiberghien, T., Bellmunt, J.: Simplifying installation and maintenance of ambient intelligent solutions toward large scale deployment. In: Chang, C.K., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 121–132. Springer, Cham (2016). doi:10.1007/978-3-319-39601-9_11
Aloulou, H., Mokhtari, M., Tiberghien, T., Biswas, J., Kenneth, L.J.H.: A semantic plug&play based framework for ambient assisted living. In: Donnelly, M., Paggetti, C., Nugent, C., Mokhtari, M. (eds.) ICOST 2012. LNCS, vol. 7251, pp. 165–172. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30779-9_21
Luukko, P.J.J., et al.: Introducing libeemd: a program package for performing the ensemble empirical mode decomposition. Computational Statistics 31(2), 545–557 (2016)
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Sadek, I., Bellmunt, J., Kodyš, M., Abdulrazak, B., Mokhtari, M. (2017). Novel Unobtrusive Approach for Sleep Monitoring Using Fiber Optics in an Ambient Assisted Living Platform. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Enhanced Quality of Life and Smart Living. ICOST 2017. Lecture Notes in Computer Science(), vol 10461. Springer, Cham. https://doi.org/10.1007/978-3-319-66188-9_5
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