Applied Technologies and Innovations

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Volume 13
Issue 1
Title Wireless sensor network for telemonitoring and home support for elderly people with chronic diseases
Author Dario Weitz, Franco Lianza, Denis Maria, Nicole Schmidt, Juan Pablo Nant
Abstract
Ageing significantly increases the prevalence of chronic diseases. Public health services expenditures grow in a biased way because chronic diseases are responsible for 50% of healthcare costs. Telemonitoring and Home Support Systems (THSS) can act as early warning systems trying to forecast the worsening or exacerbation of chronic conditions. We propose a wireless sensor network for THSS of an adult living alone in his habitual residence. The system is evaluated by means of a discrete event simulation model using the activity scanning approach. Graphics from sensor data allow to present summarized reports to family and healthcare providers through web applications. 


Citation
References
Cardinaux, F., Brownsell, S., Bradley, D., Hawley, M. S. (2013). A home daily activity simulation model for the evaluation of lifestyle monitoring systems, Computers in Biology and Medicine. 43, 1428-1436. https://doi.org/10.1016/j.compbiomed.2013.07.007
 
CASAS (2016). Center for Advanced Studies in Adaptive Systems. Retrieved September 5, 2016 from http://casas.wsu.edu/research-projects
 
Coronel, A., Feldman, S. R., Jozami, E., Kehoe, F., Piacentini, R. D., Dubbeling, M., Escobedo, F. J. (2015). Effect of urban green areas on air temperature in a medium size Argentine city. Environment Science, 2, 803-826.
 
Ding, D., Cooper, R.A., Pasquina, P.F., Fici-Pasquina, L. (2011). Sensor technology for smart homes, Maturitas, 69, 131-136.
https://doi.org/10.1016/j.maturitas.2011.03.016
 
Elfaham, A., Hagras, H., Helal, S., Hossain, S., Woong Lee, J., Cook, D. (2010). A fuzzy based verification agent for the persim human activity simulator in ambient intelligent environments, 2010 IEEE International Conference on Fuzzy Systems (FUZZ). (pp. 1-8). https://doi.org/10.1109/FUZZY.2010.5584151
 
European Commission (2011). European Comission, Joint Research Centre, Institute for Prospective Technological Studies. EUR 24669 EN. Strategic intelligence monitor on personal health systems (SIMPHS). Market structure and innovation dynamics.
 
Evans, J. R., Olson, D. L. (1998). Introduction to simulation and risk analysis. New Jersey: Prentice Hall
 
Holland, T. M. (2016). Healthy at home: The economic benefits of remote patient monitoring. Retrieved October 10, 2016 from https://insights.samsung.com/2016/02/05/healthy-at-home-the-economic-benefits-of-remote-patient-monitoring/
 
Kormanyos, B., Pataki, B. (2013). Multilevel simulation of daily activities: Why and how. 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), (pp. 15-17).
https://doi.org/10.1109/CIVEMSA.2013.6617386
 
Massachusetts Institute of Technology (2015) Media Lab Project (April 2015). Retrieved April 15, 2015 from http://www.media.mit.edu/files/projects.pdf
 
University of Florida (2016). Mobile and pervasive computing research. University of Florida. Retrieved May 12, 2016 from http://www.icta.ufl.edu/gt.htm
 
Ozkul, T., Sevin, A. (2014). Survey of popular networks used for biosensors, Biosensors Journal, 3(1) 1-5.
 
Paré, G., Jaana, M., Sicotte, C. (2007). Systematic review of home telemonitoring for chronic diseases: The evidence base, Journal of the American Medical Informatics Association, 14(3), 269-277. https://doi.org/10.1197/jamia.M2270
 
Phillips Enterprise Telehealth (2016, May 11). Why Philips for enterprise telehealth solutions. Retrieved May 11, 2016 from http://www.usa.philips.com/healthcare/solutions/enterprise-telehealth
 
Samsung (2016, May 13). Business Home, Telecare. Retrieved May 13, 2016 from http://www.samsung.com/us/business/by-industry/healthcare/
 
Schnotz, W., Lowe, R. K. (2008). A unified view of learning from animated and static graphics. In: Lowe, R. K. & Schnotz, W. (Eds.) Learning with animation: Research implications for design (pp. 304-356). New York: Cambridge University Press,
 
Suryawanshi, C. R., Bhute, Y. C. (2014). A WSN based system for enhancing intra mobility solutions for healthcare - A review. International Journal of Computer Sciences and Engineering, 2 (9), 33-37
 
UCI (2016). Machine Learning Repository. Retrieved August 17, 2016 from http://archive.ics.uci.edu/ml/datasets.html?sort=nameUp&view=list
 
United Nations (2012). World population prospects: The 2012 revision, highlights and advance tables. Working Paper No. ESA/P/WP.228
Keywords Telemonitoring and home support, remote healthcare, wireless sensor network, simulation model
DOI http://dx.doi.org/10.15208/ati.2017.01
Pages 1-11
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