Monitoring hand hygiene with commercial gas sensors: A pattern recognition approach

Abstract Insufficient hand hygiene compliance of Health care workers is a widespread problem in medicine leading to several preventable infections each year. Hence, World Health Organization (WHO) presumes, that up to 1 in 10 patients acquires a Health care associated infection (HCAI) while receiving treatment in health care facilities. Apart from hand hygiene adherence and compliance, a correct hand disinfection technique regarding the amount of hand rub and exposure time is crucial for hand hygiene efficacy. Since alcohol-based hand rub is prevalent in the medical sector, the present study presents a new method for anonymous, electronic hand hygiene monitoring with commercial Ethanol gas sensors. In contrast to previous attempts of electronic monitoring, which mostly merely count the amount of hand hygiene actions, the present approach exploits different algorithms of Machine Learning to assess and categorize the hand disinfection technique into correct and incorrect. To demonstrate functionality of the mentioned approach, labeled hand disinfection data was collected in a pilot study over the period of one year with two different sensor setups. In detail, on the one hand a commercial portable gas sensor tag is used, which can be directly attached to the surgical gown. On the other hand, the performance of several commercial gas sensors is investigated, which are stationarily mounted next to the hand rub dispenser station. The present study follows three subgoals: Firstly, suitability of the stationary vs. portable setup shall be assessed. Secondly, the adequacy of different commercial gas sensor types is evaluated and thirdly the performance of possible algorithms for time series classification of the given sensor response is investigated, i.e. Support Vector Machines (SVM) as well as different Deep Learning algorithms, such as Multilayer Perceptron (MLP), Fully Convolutional Neural Networks (FCN) and a Bidirectional Long-Short-Term Memory network (Bi-LSTM). It could be found that the stationary setup is significantly superior compared to the portable setup. For most of the sensors within this stationary setup an accuracy score between 85% and 87% could be obtained for classification of correctly and incorrectly performed hand disinfections.