Abstract
Patients spend the vast majority of their hospital stay in an unmonitored bed where various mobility factors can impact patient safety and quality. Specifically, bed positioning and a patient's related mobility in that bed can have a profound impact on risks such as pneumonias, blood clots, bed ulcers and falls. This issue has been exacerbated as the nurse-per-bed (NPB) ratio has decreased in recent years. To help assess these risks, it is critical to monitor a hospital bed's positional status (BPS). Two bed positional statuses, bed height (BH) and bed chair angle (BCA), are of critical interests for bed monitoring. In this paper, we develop a bed positional status detection system using a single Microsoft Kinect. Experimental results show that we are able to achieve 94.5% and 93.0% overall accuracy of the estimated BCA and BH in a simulated patient's room environment.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5900-5903 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781424479290 |
| DOIs | |
| State | Published - Nov 2 2014 |
| Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014 |
Publication series
| Name | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
|---|
Other
| Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 8/26/14 → 8/30/14 |
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ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
- Biomedical Engineering
- Medicine(all)
Cite this
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Detection of patient's bed statuses in 3D using a Microsoft Kinect. / Li, Yun; Berkowitz, Lyle; Noskin, Gary A; Mehrotra, Sanjay.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 5900-5903 6944971 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Detection of patient's bed statuses in 3D using a Microsoft Kinect
AU - Li, Yun
AU - Berkowitz, Lyle
AU - Noskin, Gary A
AU - Mehrotra, Sanjay
PY - 2014/11/2
Y1 - 2014/11/2
N2 - Patients spend the vast majority of their hospital stay in an unmonitored bed where various mobility factors can impact patient safety and quality. Specifically, bed positioning and a patient's related mobility in that bed can have a profound impact on risks such as pneumonias, blood clots, bed ulcers and falls. This issue has been exacerbated as the nurse-per-bed (NPB) ratio has decreased in recent years. To help assess these risks, it is critical to monitor a hospital bed's positional status (BPS). Two bed positional statuses, bed height (BH) and bed chair angle (BCA), are of critical interests for bed monitoring. In this paper, we develop a bed positional status detection system using a single Microsoft Kinect. Experimental results show that we are able to achieve 94.5% and 93.0% overall accuracy of the estimated BCA and BH in a simulated patient's room environment.
AB - Patients spend the vast majority of their hospital stay in an unmonitored bed where various mobility factors can impact patient safety and quality. Specifically, bed positioning and a patient's related mobility in that bed can have a profound impact on risks such as pneumonias, blood clots, bed ulcers and falls. This issue has been exacerbated as the nurse-per-bed (NPB) ratio has decreased in recent years. To help assess these risks, it is critical to monitor a hospital bed's positional status (BPS). Two bed positional statuses, bed height (BH) and bed chair angle (BCA), are of critical interests for bed monitoring. In this paper, we develop a bed positional status detection system using a single Microsoft Kinect. Experimental results show that we are able to achieve 94.5% and 93.0% overall accuracy of the estimated BCA and BH in a simulated patient's room environment.
UR - http://www.scopus.com/inward/record.url?scp=84929458528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929458528&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6944971
DO - 10.1109/EMBC.2014.6944971
M3 - Conference contribution
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 5900
EP - 5903
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -