Human Activity Recognition Framework in Monitored Environments

O. León, M. P. Cuellar, M. Delgado, Y. Le Borgne, G. Bontempi

Abstract

This work addresses the problem of the recognition of human activities in Ambient Assisted Living (AAL) scenarios. The ultimate goal of a good AAL system is to learn and recognise behaviours or routines of the person or people living at home, in order to help them if something unusual happens. In this paper, we explore the advances in unobstrusive depth camera-based technologies to detect human activities involving motion. We explore the benefits of a framework for gesture recognition in this field, in contrast to raw signal processing techniques. For the framework validation, Hidden Markov Models and Dynamic Time Warping have been implemented for the action learning and recognition modules as a baseline due to their well known results in the field. The results obtained after the experimentation suggest that the depth sensors are accurate enough and useful in this field, and also that the preprocessing framework studied may result in a suitable methodology.

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Paper Citation


in Harvard Style

León O., P. Cuellar M., Delgado M., Le Borgne Y. and Bontempi G. (2014). Human Activity Recognition Framework in Monitored Environments . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 487-494. DOI: 10.5220/0004755504870494


in Bibtex Style

@conference{icpram14,
author={O. León and M. P. Cuellar and M. Delgado and Y. Le Borgne and G. Bontempi},
title={Human Activity Recognition Framework in Monitored Environments},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={487-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004755504870494},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Human Activity Recognition Framework in Monitored Environments
SN - 978-989-758-018-5
AU - León O.
AU - P. Cuellar M.
AU - Delgado M.
AU - Le Borgne Y.
AU - Bontempi G.
PY - 2014
SP - 487
EP - 494
DO - 10.5220/0004755504870494