Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence. Methods. We used multiple Microsoft Kinects to track the 3-dimensional movement of HCWs and their hands within hospital rooms. We applied computer vision techniques to recognize and determine the position of fiducial markers attached to the patient's bed to

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... ne the location of the HCW's hands with respect to the bed. To measure our system's ability to detect HCW-patient contacts, we counted each time a HCW's hands entered a virtual rectangular box aligned with a patient bed. To measure PPE adherence, we identified the hands, torso, and face of each HCW on room entry, determined the color of each body area, and compared it with the color of gloves, gowns, and face masks. We independently examined a ground truth video recording and compared it with our system's results. Results. Overall, for touch detection, the sensitivity was 99.7%, with a positive predictive value of 98.7%. For gowned entrances, sensitivity was 100.0% and specificity was 98.15%. For masked entrances, sensitivity was 100.0% and specificity was 98.75%; for gloved entrances, the sensitivity was 86.21% and specificity was 98.28%. Conclusions. Using computer vision and depth sensing, we can estimate potential HH opportunities at the bedside and also estimate adherence to PPE. Our fine-grained estimates of how and how often HCWs interact directly with patients can inform a wide range of patient-safety research.