Monitoring patients in hospital beds using unobtrusive depth sensors.
Annu Int Conf IEEE Eng Med Biol Soc
; 2014: 5904-7, 2014.
Article
en En
| MEDLINE
| ID: mdl-25571340
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.
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Bases de datos:
MEDLINE
Asunto principal:
Lechos
/
Monitoreo Fisiológico
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2014
Tipo del documento:
Article