  There are six, well-structured hand gestures for washing hands as provided by
World Health Organisation guidelines. In this paper, hand features such as
contours of the hands, the centroid of the hands, and extreme hand points along
the largest contour are extracted for specific hand-washing gestures with the
use of a computer vision library, OpenCV. For this project, a robust dataset of
hand hygiene video recordings is built with the help of 30 research
participants. In this work, a subset of the dataset was used as a pilot study
to demonstrate the effectiveness of the KNN algorithm. Extracted hand features
saved in a CSV file are passed to a KNN model with a cross-fold validation
technique for the classification and prediction of the unlabelled data. A mean
accuracy score of >95% is achieved and proves that the KNN algorithm with an
appropriate input value of K=3 is efficient for hand hygiene gestures
classification.
