Singh, AmitHaque, AlbertAlahi, AlexandreYeung, serenaGuo, michelleGlassman, jillBeninati, williamPlatchek, terryFei-Fei, LiMilstein, Arnold2020-11-012020-11-012020-11-01202010.1093/jamia/ocaa115https://infoscience.epfl.ch/handle/20.500.14299/172924Hand 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.computer visionhand hygienedeep learningtext::journal::journal article::research article