Paper Info
Title
Monitoring patients in hospital beds using unobtrusive depth sensors.
Abstract
We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner. An existing fall detection algorithm is currently generating fall alerts in several rooms in the University of Missouri Hospital (MUH). In this paper we describe a technique to reduce false alerts such as pillows falling off the bed or equipment movement. We do so by detecting the presence of the patient in the bed for the times when the fall alert is generated. We test our algorithm on 96 hours obtained in two hospital rooms from MUH.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944972
EMBC
Keywords
Field
DocType
biomechanics,biomedical telemetry,remote sensing,equipment movement recognition,hospital rooms,telemedicine,alarm systems,patient monitoring,image segmentation,patient privacy,kinect sensor,fall alert generation,hospitals,nonintrusive patient monitoring,false alert reduction,night time activity segmentation,accidents,hospital beds,unobtrusive depth sensors,feature extraction,patient presence detection,day time activity segmentation,remote patient monitoring,furniture,fall detection algorithm,patient activity recognition,medical image processing,pillow falling recognition,image motion analysis
Computer vision,Activity recognition,Computer science,Segmentation,Artificial intelligence
Conference
Volume
ISSN
Citations 
2014
1557-170X
2
PageRank 
References 
Authors
0.39
0
6
Name
Order
Citations
PageRank
Tanvi Banerjee18316.41
Moein Enayati241.80
James M. Keller33201436.69
Marjorie Skubic41045105.36
Mihail Popescu581.97
Marilyn Rantz631026.24