Abstract
With the increasing aging population, the intelligent monitoring for the elderly living alone has become a hot research topic. As the universal of WiFi, the intelligent monitoring system based on WiFi Channel State Information (CSI) is proposed for elderly, including fall detection and sleep monitoring. The system is completely off-the-shelf and non-contact, which is more convenient and comfortable to users. The fall detection algorithm is based on phase difference and amplitude of CSI, whose features of different motions are extracted, and the falls are detected by machine learning algorithm. The sleep monitoring system only used phase difference changes of antennas; meanwhile, the respiration, turning over, and apnea will be detected, which can be used to evaluate sleep quality as well. According to the results of experiments, the accuracy of the system can reach 84.6% in fall detection. In sleep monitoring, the error of each breath is 0.337 s and the average accuracy is 89.9%.
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References
WHO Homepage: http://www.portal.pmnch.org/mediacentre/factsheets/fs404/en/. Accessed 21 May 2017
Sullivan, C.E., Lynch, C.: Device and method for nonclinical monitoring of breathing during sleep, control of CPAP treatment and preventing apnea. US, US6398739 (2002)
Wang, H., Zhang, D., Wang, Y., Ma, J., Wang, Y., Li, S.: RT-Fall: a real-time and contactless fall detection system with commodity WiFi devices. IEEE Trans. Mob. Comput. 16(2), 511–526 (2017)
Khan, S., Hoey, J.: Review of fall detection techniques: a data availability perspective. Med. Eng. Phys. 39, 12–22 (2016)
Westergren, A., Hagell, P., Hammarlund, C.S.: Malnutrition and risk of falling among elderly without home-help service -a cross sectional study. J. Nutr. Heal. Aging 18(10), 1–7 (2014)
Aziz, O., Musngi, M., Park, E.J., Mori, G., Robinovitch, S.N.: A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med. Biol. Eng. Comput. 55(1), 45–55 (2017)
Bourke, A.K., Ihlen, E.A., Van de Ven, P., Nelson, J., Helbostad, J.L., Bourke, A.K., et al.: Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor. In: Conference Proceedings of IEEE Engineering in Medicine and Biology Society, vol. 2016, p. 4881 (2016)
Chen, K.H., Hsu, Y.W., Yang, J.J., Jaw, F.S.: Enhanced characterization of an accelerometer-based fall detection algorithm using a repository. Instrum Sci. Technol. 45, 382–391 (2017)
Caccami, M.C., Mulla, M.Y.S., Di Natale, C., et al.: Wireless monitoring of breath by means of a graphene oxide-based radiofrequency identification wearable sensor. In: 2017 11th European Conference on Antennas and Propagation (EUCAP), pp. 3394–3396. IEEE (2017)
Gao, T., Bishop, W.E., Juang, R.R., et al.: Sensor-based adaptive wearable devices and methods. US, US7629881 (2009)
Nam, Y., Kim, Y., Lee, J.: Sleep monitoring based on a tri-axial accelerometer and a pressure sensor. Sensors 16(5), 750 (2016)
Looney, D., Goverdovsky, V., Rosenzweig, I., et al.: Wearable in-ear encephalography sensor for monitoring sleep. Prelim. Obs. Nap Stud. 13(12), 2229–2233 (2016). Annals of the American Thoracic Society
Sen, S., Radunovic, B., Choudhury, R.R., et al.: You are facing the Mona Lisa: spot localization using PHY layer information. In: International Conference on Mobile Systems, Applications, and Services, pp. 183–196. ACM (2012)
Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) Nonlinear estimation and classification. Lecture Notes in Statistics, vol. 171, pp. 149–171. Springer, New York (2003). https://doi.org/10.1007/978-0-387-21579-2_9
Rechtschaffen, A., Kales, A.: A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. US Government Printing Office, US Public Health Service, Washington DC (1968)
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This research is financially supported under the Scientific Innovation and Venture Incubation.
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Bao, N. et al. (2018). The Intelligent Monitoring for the Elderly Based on WiFi Signals. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_85
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DOI: https://doi.org/10.1007/978-3-319-77380-3_85
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