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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 4073584, 17 pages
Research Article

Human Activity Recognition in AAL Environments Using Random Projections

1Department of Software Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
2Institute of Mathematics, Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland

Received 8 February 2016; Revised 29 April 2016; Accepted 19 May 2016

Academic Editor: Ezequiel López-Rubio

Copyright © 2016 Robertas Damaševičius et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.