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Hand hygiene is one of the most significant factors in preventing hospitalacquired infections (HAI) which often be transmitted by medical staffs incontact with patients in the operating room (OR). Hand hygiene monitoring couldbe important to investigate and reduce the outbreak of infections within theOR. However, an effective monitoring tool for hand hygiene compliance isdifficult to develop due to the visual complexity of the OR scene. Recentprogress in video understanding with convolutional neural net (CNN) hasincreased the application of recognition and detection of human actions.Leveraging this progress, we proposed a fully automated hand hygiene monitoringtool of the alcohol-based hand rubbing action of anesthesiologists on OR videousing spatio-temporal features with 3D CNN. First, the region of interest (ROI)of anesthesiologists' upper body were detected and cropped. A temporalsmoothing filter was applied to the ROIs. Then, the ROIs were given to a 3D CNNand classified into two classes: rubbing hands or other actions. We observedthat a transfer learning from Kinetics-400 is beneficial and the optical flowstream was not helpful in our dataset. The final accuracy, precision, recalland F1 score in testing is 0.76, 0.85, 0.65 and 0.74, respectively.

Fully Automated Hand Hygiene Monitoring\\in Operating Room using 3D Convolutional Neural Network