Abstract
This paper presents a real solution for detecting abnormal situations at home environments, mainly oriented to living alone and elderly people. The aim of the work described in this paper is, first, to reduce the raw data about the situation of the elder at home, tracking only the relevant signals, and second, to predict the regular situation of the person at home, checking if its situation is normal or abnormal. The challenge in this work is to transform the real word complexity of the user patterns using only “lazy” sensor data (position sensors) in a real scenario over several homes. We impose two restrictions to the system (lack of “a priori” information about the behavior of the elderly and the absence of historic database) because the aim of this system is to build an automatic environment and study the minimal historical data to achieve an accurate predictive model, in order to generate a commercial produtc working fully few weeks after the installation.
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The research was supported by the REAAL project (CIP ICT PSP – 2012 - 325189).
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Moreno-Fernandez-de-Leceta, A., Lopez-Guede, J.M., Graña, M., Cantera, J.C. (2017). Real Prediction of Elder People Abnormal Situations at Home. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_4
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DOI: https://doi.org/10.1007/978-3-319-47364-2_4
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