Hand washing is a critical activity in preventing the spread of infection in healthcare environments and contamination in food processing industries. The World Health Organization (WHO) guidelines recommend a hand washing protocol consisting of different hand washing poses to ensure that all parts of the hands are thoroughly cleaned. The assessment and training of workers in proper hand hygiene technique is performed by human trainers and can be very tedious and time consuming. In this paper we present an automated computer vision system which measures the user's hand washing technique to ensure that the WHO guidelines are correctly followed. The main contribution of this work is a system which performs robust hand segmentation and hand washing pose classification. The performance of the system is analysed based on data collected in a clinical trial performed in a hospital ward. The performance of the system was compared with that of human auditors. The agreement between two human auditors and between the system and the human auditors were found to be of the same order, which emphasizes the accuracy and validity of the proposed automated system.
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