Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system

Author

Hsia, C.C. ; Liou, K.J. ; Aung, A.P.W. ; Foo, V. ; Huang, W. ; Biswas, J.

Author_Institution

ICT-Enabled Healthcare Program, ITRI South, Tainan City, Taiwan

fYear

2009

fDate

3-6 Sept. 2009

Firstpage

6131

Lastpage

6134

Abstract

Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.

Keywords

medical signal processing; patient care; pressure sensors; principal component analysis; sensor fusion; signal classification; sleep; support vector machines; Kurtosis; PCA; SVM; bedridden patient; pressure sensitive bed system; pressure ulcer; principal component analysis; skewness estimation; sleeping posture classification; support vector machine; Sleeping Posture; bayesian classification; pressure sensor; Algorithms; Artificial Intelligence; Beds; Diagnosis, Computer-Assisted; Equipment Design; Equipment Failure Analysis; Humans; Manometry; Pattern Recognition, Automated; Posture; Pressure; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Sleep;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Conference_Location

Minneapolis, MN

ISSN

1557-170X

Print_ISBN

978-1-4244-3296-7

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2009.5334694

Filename

5334694