Automatic detection of hand hygiene using computer vision technology.

ABSTRACT:

Objective

Hand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more accurate appraisals. Our primary objective was to evaluate if a computer vision algorithm could accurately observe hand hygiene dispenser use in images captured by depth sensors.

Materials and methods

Sixteen depth sensors were installed on one hospital unit. Images were collected continuously from March to August 2017. Utilizing a convolutional neural network, a machine learning algorithm was trained to detect hand hygiene dispenser use in the images. The algorithm's accuracy was then compared with simultaneous

SUBMITTER: Singh A 

PROVIDER: S-EPMC7481030 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature