Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny

TL;DR

This paper presents a real-time, cost-effective system using Yolov4-tiny for detecting PPE donning and doffing sequences, providing immediate feedback to healthcare workers to improve safety and adherence to protocols.

Contribution

It introduces a novel real-time detection system combining object detection with sequencing algorithms using Yolov4-tiny for healthcare PPE procedures.

Findings

Effective real-time detection of PPE donning/doffing sequences

Cost-efficient deployment in healthcare settings

Improved compliance with PPE protocols

Abstract

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.