Authors:
O. León
1
;
M. P. Cuellar
1
;
M. Delgado
1
;
Y. Le Borgne
2
and
G. Bontempi
2
Affiliations:
1
University of Granada, Spain
;
2
Universit Libre de Bruxelles, Belgium
Keyword(s):
Human Activity Recognition, Ambient Assisted Living, Vision Computing, Data Mining.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image Understanding
;
Learning of Action Patterns
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Telecommunications
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.