Recognition of hand disinfection by an alcohol-containing gel using two-dimensional imaging in a clinical setting

Hand hygiene compliance is important for the prevention of healthcare-associated infections. The conventional method of measuring hand disinfection guidelines involves an external observer watching the staff personnel, which introduces bias, and observations are only made for a set period of time. An unbiased, non-invasive automated system for assessing hand sanitization actions can provide a better estimate of compliance.

To develop an automated detector to assess hand hygiene compliance in hospitals, without bias from an external observer, capable of making observations at different times of the day, as non-invasive as possible by using only one camera, and collecting as much information as possible from two-dimensional video footage.

Video footage with annotations from various sources was collected to determine when staff performed hand disinfection with gel-based alcohol. The frequency response of wrist movement was used to train a support vector machine to identify hand sanitization events.

This system detected sanitization events with an accuracy of 75.18%, a precision of 72.89%, and a recall of 80.91%. These metrics provide an overall estimate of hand sanitization compliance without bias due to the presence of an external observer while collecting data over time.

Investigation of these systems is important because they are not constrained by time-limited observations, are non-invasive, and they eliminate observer bias. Although there is room for improvement, the proposed system provides a fair assessment of compliance that the hospital can use as a reference to take appropriate action.