Several studies in the mid-19th century indicate the impact of hand hygiene compliance on the decline of infections in the health sector. However, despite this knowledge and world-wide accepted guidelines on hand hygiene, infection rates in today's hospitals are still too high. For, example, the number of deaths related to infections with multi-resistant pathogens is 15,000 persons per year in Germany. Studies indicate an average compliance rate of hand hygiene guidelines of 41.2 to 55.2 per cent. Required hand disinfection steps are either omitted or performed too shortly. For example, the actual time spent during the hand disinfection process is often below 15 seconds, although the norm EN 1500 proposes 30 seconds.
The Institute of Measurement and Automatic Control is developing a system which evaluates the quantity and quality of hand disinfection processes using an optical sensor and computer vision. The process of hand disinfection can be divided into sequences of gestures. The system is learning to distinguish those gestures during a training phase which is commonly known as machine learning. Attributes such as gesture duration, kind, quantity and succession are recognized and evaluated. Finally, the system aims to support the user in real time at the hand disinfection process and informs about potential untreated areas.