Development of a system for monitoring and validation of proper hand washing using machine learning

Background: Hand hygiene is critical in medical settings to prevent infections and ensure patient safety.Despite multiple initiatives to improve adherence to hygiene protocols, compliance rates remain low, posing a persistent challenge.Methods: We developed an AI-driven application to monitor adherence to hand-rubbing techniques in a clinical setting.The system utilizes machine learning to an alyze hand landmarks extracted from video, incorporating a normalization process based on hand centroids to mitigate biases related to camera distance and hand size.Three machine learning models-Logistic Regression, Support Vector Machine, and Random Forest-were evaluated based on accuracy, inference speed, and memory usage.Results: Logistic Regression demonstrated the best performance, achieving 99.5 % accuracy and processing each hand-washing step in only 3 ms.The application also tracks the duration of each hygiene step, promoting compliance with recommended hand-washing times.We tested the algorithm in a clinical setting.Conclusion: This AI-driven solution provides a scalable, real-time method for improving hand hygiene compliance in clinical settings.Its ability to deliver data-supported feedback highlights its potential to enhance patient safety and reduce infection rates.