Feature Extraction, Classification and Prediction for Hand Hygiene Gestures with KNN Algorithm
This work addresses hand hygiene monitoring for health applications, but it is incremental as it applies an existing method (KNN) to a new dataset.
The paper tackled the problem of classifying hand hygiene gestures from video using computer vision features and the KNN algorithm, achieving a mean accuracy score of over 95%.
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.