Vision-based detection of unusual patient activity

Borges, Paulo Vinicius Koerich and Nourani-Vatani, Navid (2011). Vision-based detection of unusual patient activity. In: David P. Hansen, Anthony J. Maeder and Louise K. Schaper, Health informatics : The transformative power of innovation : Selected papers from the 19th Australian National Health Informatics Conference (HIC 2011). 19th Australian National Health Informatics Conference, Brisbane QLD, Australia, (16-23). 1-5 August 2011.

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Author Borges, Paulo Vinicius Koerich
Nourani-Vatani, Navid
Title of paper Vision-based detection of unusual patient activity
Conference name 19th Australian National Health Informatics Conference
Conference location Brisbane QLD, Australia
Conference dates 1-5 August 2011
Proceedings title Health informatics : The transformative power of innovation : Selected papers from the 19th Australian National Health Informatics Conference (HIC 2011)   Check publisher's open access policy
Journal name Studies in Health Technology and Informatics   Check publisher's open access policy
Place of Publication Amsterdam, The Netherlands
Publisher I O S Press
Publication Year 2011
Sub-type Fully published paper
DOI 10.3233/978-1-60750-791-8-16
ISBN 9781607507901
ISSN 0926-9630
1879-8365
Editor David P. Hansen
Anthony J. Maeder
Louise K. Schaper
Volume 168
Start page 16
End page 23
Total pages 8
Collection year 2012
Language eng
Formatted Abstract/Summary Automated patient monitoring in hospital environments has gained increased attention in the last decade. An important problem is that of behaviour analysis of psychiatric patients, where adequate monitoring can minimise the risk of harm to hospital staff, property and to the patients themselves. For this task, we perform a preliminary investigation on visual-based patient monitoring using surveillance cameras. The proposed method uses statistics of optical flow vectors extracted from the patient movements to identify dangerous behaviour. In addition, the method also performs foreground segmentation followed by blob tracking in order to extract shape and temporal characteristics of blobs. Dangerous behaviour includes attempting to break out of safe-rooms, self-harm and fighting. The features considered include a temporal and multi-resolution analysis of blob coarseness, blob area, movement speed and position in the room. This information can also be used to normalise the other features according to estimated position of the patient in the room. In this preliminary study, experiments in a real hospital scenario illustrate the potential applicability of the method.
Keyword Patient monitoring
Computer vision
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Mon, 25 Jun 2012, 10:06:28 EST by Paulo Borges on behalf of School of Information Technol and Elec Engineering