Citation

Chaaraoui AA, Padilla-López JR, Ferrández-Pastor FJ, Nieto-Hidalgo M, Flórez-Revuelta F. Sensors (Basel) 2014; 14(5): 8895-8925.

Affiliation

Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road,Kingston upon Thames KT1 2EE, UK. F.Florez@kingston.ac.uk.

Copyright

(Copyright © 2014, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s140508895

PMID

24854209

Abstract

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services.

Language: en