Feature Detection for Hand Hygiene Stages
This work addresses hand hygiene monitoring for health and safety applications, but it is incremental as it builds on existing computer vision methods without introducing new techniques.
The paper tackled the problem of detecting hand hygiene stages by constructing an aluminum rig to create a dataset and using image processing algorithms like Harris detector, Shi-Tomasi, and SIFT for hand pose extraction and feature detection, with preliminary results demonstrated on the 'rub hands palm to palm' pose.
The process of hand washing involves complex hand movements. There are six principal sequential steps for washing hands as per the World Health Organisation (WHO) guidelines. In this work, a detailed description of an aluminium rig construction for creating a robust hand-washing dataset is discussed. The preliminary results with the help of image processing and computer vision algorithms for hand pose extraction and feature detection such as Harris detector, Shi-Tomasi and SIFT are demonstrated. The hand hygiene pose- Rub hands palm to palm was captured as an input image for running all the experiments. The future work will focus upon processing the video recordings of hand movements captured and applying deep-learning solutions for the classification of hand-hygiene stages.