Feature Identification and Matching for Hand Hygiene Pose
This incremental work addresses pose classification for hand hygiene monitoring, potentially aiding in healthcare compliance.
The paper compared SIFT, SURF, and ORB feature descriptors for matching hand hygiene poses, finding that ORB achieved the highest number of correct matches in less time.
Three popular feature descriptors of computer vision such as SIFT, SURF, and ORB compared and evaluated. The number of correct features extracted and matched for the original hand hygiene pose-Rub hands palm to palm image and rotated image. An accuracy score calculated based on the total number of matches and the correct number of matches produced. The experiment demonstrated that ORB algorithm outperforms by giving the high number of correct matches in less amount of time. ORB feature detection technique applied over handwashing video recordings for feature extraction and hand hygiene pose classification as a future work. OpenCV utilized to apply the algorithms within python scripts.