  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.
