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Electronic Recognition of Hand Hygiene Technique and Duration

Published online by Cambridge University Press:  10 May 2016

Valerie Galluzzi ,
Ted Herman ,
D. J. Shumaker ,
D. R. Macinga ,
J. W. Arbogast ,
Elena M. Segre ,
Alberto M. Segre  and
Philip M. Polgreen
Valerie Galluzzi
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Ted Herman
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
D. J. Shumaker
Affiliation:
GOJO Industries, Akron, Ohio
D. R. Macinga
Affiliation:
GOJO Industries, Akron, Ohio
J. W. Arbogast
Affiliation:
GOJO Industries, Akron, Ohio
Elena M. Segre
Affiliation:
Department of Health and Human Physiology, University of Iowa, Iowa City, Iowa
Alberto M. Segre
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Philip M. Polgreen*
Affiliation:
Departments of Internal Medicine and Epidemiology, University of Iowa, Iowa City, Iowa
*
MPH, Carver College of Medicine, Department of Internal Medicine, University of Iowa, Iowa City, IA 52242 ([email protected]).
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Abstract

We captured 3-dimensional accelerometry data from the wrists of 116 healthcare professionals as they performed hand hygiene (HH). We then used these data to train a k-nearest-neighbors classifier to recognize specific aspects of HH technique (ie, fingertip scrub) and measure the duration of HH events.

Infect Control Hosp Epidemiol 2014;35(10):1298–1300

Type
Concise Communication
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 10 , October 2014 , pp. 1298 - 1300
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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References

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