Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny
This addresses safety for healthcare workers and patients by automating PPE protocol compliance, though it is incremental as it applies an existing method (Yolov4-tiny) to a new application domain.
The paper tackles the problem of ensuring proper personal protective equipment (PPE) donning and doffing by healthcare workers, using a real-time object detection system with Yolov4-tiny and sequencing algorithms to provide alerts for missed steps, achieving feasibility and cost-effectiveness for deployment in healthcare settings.
Maintaining patient safety and the safety of healthcare workers (HCWs) in hospitals and clinics highly depends on following the proper protocol for donning and taking off personal protective equipment (PPE). HCWs can benefit from a feedback system during the putting on and removal process because the process is cognitively demanding and errors are common. Centers for Disease Control and Prevention (CDC) provided guidelines for correct PPE use which should be followed. A real time object detection along with a unique sequencing algorithms are used to identify and determine the donning and doffing process in real time. The purpose of this technical research is two-fold: The user gets real time alert to the step they missed in the sequence if they don't follow the proper procedure during donning or doffing. Secondly, the use of tiny machine learning (yolov4-tiny) in embedded system architecture makes it feasible and cost-effective to deploy in different healthcare settings.