Research on IoT-based Real-time Monitoring of Operating Room Air Cleanliness and Precise Prevention and Control of Infection Risk
TL;DR
An IoT-based real-time monitoring network was designed and deployed with 12 high-precision nodes to continuously collect particulate matter concentration, microbial concentration, temperature, humidity, pressure difference, and personnel activity, offering a reliable technical approach for reducing surgical site infection risks in digitally monitored operating-room environments.
Abstract
Operating room air cleanliness is critical for infection control, yet conventional static monitoring cannot capture the dynamic release of textile fibers from surgical drapes and medical linens during operations, limiting timely prevention. In this study, an IoT-based real-time monitoring network was designed and deployed with 12 high-precision nodes to continuously collect particulate matter concentration, microbial concentration, temperature, humidity, pressure difference, and personnel activity. Considering the dense medical-equipment environment of operating rooms, the wireless sensing architecture was configured for stable radio-frequency data transmission and electromagnetic-compatible operation, providing a practical sensing scenario related to electromagnetic wave propagation in indoor medical spaces. A dynamic infection-risk assessment model was then established through data fusion, and a long short-term memory algorithm was introduced to predict short-term risk trends. A graded intelligent warning mechanism was connected to the air-conditioning and purification system to support on-demand control. By tracking particulate matter and fiber shedding associated with medical textiles, the system achieved real-time perception, risk prediction, and closed-loop intervention for air cleanliness, offering a reliable technical approach for reducing surgical site infection risks in digitally monitored operating-room environments.