Hand Hygiene

Hand hygiene research focuses on improving the accuracy and efficiency of assessing handwashing techniques, primarily to reduce healthcare-associated infections and promote better hygiene practices, especially among children. Current research employs computer vision techniques, leveraging deep learning models like convolutional neural networks (CNNs), including variations such as U-Net and Inception-ResNet, and algorithms like K-Nearest Neighbors (KNN), to analyze video recordings of handwashing, achieving high accuracy in gesture recognition and step segmentation. These advancements offer potential for automated feedback systems in training and real-time monitoring, improving both the effectiveness of hand hygiene education and the reliability of its assessment.