Veralia Gabriela Sánchez
Department of Electrical Engineering, Information Technology and Cybernetics, University of South-Eastern Norway (USN), Porsgrunn, Norway
Nils-Olav Skeie
Department of Electrical Engineering, Information Technology and Cybernetics, University of South-Eastern Norway (USN), Porsgrunn, Norway
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http://dx.doi.org/10.3384/ecp18153222Published in: Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway
Linköping Electronic Conference Proceedings 153:31, p. 222-229
Published: 2018-11-19
ISBN: 978-91-7685-494-5
ISSN: 1650-3686 (print), 1650-3740 (online)
Human activity recognition in smart house environments is the task of automatic recognition of physical activities of a person to build a safe environment for older adults or any person in their daily life. The aim of this work is to develop a model that can recognize abnormal activities for assisting people living alone in a smart house environment. The idea is based on the assumption that people tend to follow a specific pattern of activities in their daily life. An open source database is used to train the decision trees classifier algorithm. Training and testing of the algorithm is performed using MATLAB. The results show an accuracy rate of 88.02% in the activity detection task.
intelligent environment, behaviour modelling, pattern recognition, probabilistic model, predictive model, Norway
