Smart home simulation model for synthetic sensor datasets generation
World population is ageing due to longer life expectancy worldwide. There is a trend in elderly people to live alone in their habitual residences in spite of health and safety risks. Smart Homes, intelligent environment systems deployed at elderly homes can act as early warning systems trying to forecast the worsening or exacerbation of the resident chronic conditions. Access to sensor datasets is essential for the development of an efficient real smart home. Procurement of such datasets is subject to several restrictions and difficulties. This paper describes the generation of synthetic datasets by means of a simulation model as a suitable alternative previous to the deployment of a real monitoring system. The collection of synthetic datasets will be used during the next project step to train and evaluate activity recognition methods and algorithms.
Brownsell, S., Blackburn, S., & Hawley, M.S. (2008). An evaluation of second and third generation telecare services in older people's housing. J. Telemed Telecare, 14 (1), 8-12.
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.
CASAS, Center for Advanced Studies in Adaptive Systems. Retrieved from http://casas.wsu.edu/research-projects
European Commission. (2014). The 2015 Ageing Report. European Economy, (8), 11.
Evans, J.R. & Olson, D.L. (1998). Introduction to simulation and risk analysis. New Jersey, NJ: Prentice Hall.
Horgas, A., Wilms, H., & Baltes, M. (1998). Daily life in very old age: Everyday activities as expression of successful aging. The Gerontologist, 38(5), 556-567.
Katz, S., Ford, A.B., Moskowitz, R.W., Jackson, B.A., & Jaffe, M.W. (1963). Studies of illness in the aged: The index of ADL, a standardized measure of biological and psychosocial function. JAMA, 185, 914-919.
Law, A.M. & Kelton, W.D. (2000). Simulation modeling and analysis [3th ed.]. Singapore: McGraw-Hill
Massachusetts Institute of Technology [MIT]. (2015). Media Lab Project April 2015. Cambridge, MA: MIT. Available at: http://www.media.mit.edu/files/projects.pdf
Mobile and Pervasive Computing Research, University of Florida. Retrieved from http://www.icta.ufl.edu/gt.htm
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 -275.
Phillips Enterprise Telehealth. Retrieved from http://www.usa.philips.com/healthcare/solutions/enterprise-telehealth
Samsung SmartThings. Retrieved from https://www.smartthings.com/uk/
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, NY: Cambridge University Press.
Synnott, J., Nugent, C., & Jeffers, P. (2015). Simulation of smart home activity datasets. Sensors, 15, 14162-14179.
Torrado, S. (2004). Argentina: escenarios demográficos hacia 2025 [Informe preparado para el Programa de Estudios Prospectivos de la Secretaría para la Ciencia, la Tecnología y la Innovación Productiva (SECTIP)]. Buenos Aires, Argentina: UBA. Available at: http://www.econ.uba.ar/www/departamentos/economia/nuevo/depto/materias_depto/cursos/557_garciadefanelli/Programa%202009/Torrado%20Escenarios.pdf
Van Kasteren, T. Datasets for activity recognition. Retrieved from: https://sites.google.com/ site/tim0306/datasets
Werner, C. (2011, Nov.). The older population: 2010 [2010 Census Briefs]. Washington, DC: United States Census Bureau. Available at: https://www.census.gov/prod/cen2010/briefs/c2010br-09.pdf
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