ABSTRACT
Hand hygiene monitoring and compliance systems play a significant role in curbing the spread of healthcare associated infections and the COVID-19 virus. In this paper, a model has been developed using convolution neural networks (CNN) and computer vision to detect an individual's germ level, monitor their hand wash technique and create a database containing all records. The proposed model ensures all individuals entering a public place prevent the spread of healthcare associated infections (HCAI). In our model, the individual's identity is verified using two-factor authentication, followed by checking the hand germ level. Furthermore, if required the model will request sanitizing/ hand wash for completion of the process. During this time, the hand movements are checked to ensure each hand wash step is completed according to World Health Organization (WHO) guidelines. Upon completion of the process, a database with details of the individual's germ level is created. The advantage of our model is that it can be implemented in every public place and it is easily integrable. The performance of each segment of the model has been tested on real-time images an validated. The accuracy of the model is 100% for personal identification, 96.87% for hand detection, 93.33% for germ detection and 85.5% for the compliance system respectively.