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Sensors 2017, 17(7), 1528; doi:10.3390/s17071528

Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models

1
INRIA Sophia Antipolis, 2004 route des Lucioles, BP 93, 06902 Sophia Antipolis, France
2
CobTek-Cognition Behaviour Technology, Université Nice Sophia Antipolis, 06100 Nice, France
3
MUMC-School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, 6200 Maastricht, The Netherlands
*
Authors to whom correspondence should be addressed.
Received: 29 April 2017 / Revised: 21 June 2017 / Accepted: 23 June 2017 / Published: 29 June 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Abstract

Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease. View Full-Text
Keywords: activity recognition; activities of daily living; assisted living; color-depth sensing; complex events; people detection and tracking; knowledge representation; senior monitoring activity recognition; activities of daily living; assisted living; color-depth sensing; complex events; people detection and tracking; knowledge representation; senior monitoring
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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. (CC BY 4.0).

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MDPI and ACS Style

Crispim-Junior, C.F.; Gómez Uría, A.; Strumia, C.; Koperski, M.; König, A.; Negin, F.; Cosar, S.; Nghiem, A.T.; Chau, D.P.; Charpiat, G.; Bremond, F. Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models. Sensors 2017, 17, 1528.

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