A framework for robot-assisted doffing of personal protective equipment

Abstract

When treating highly-infectious diseases such as Ebola, health workers are at high risk of infection during the doffing of Personal Protective Equipment (PPE). This is due to factors such as fatigue, hastiness, and inconsistency in training. The introduction of a semi-autonomous robot doffing assistant has the potential to increase the safety of the doffing procedure by assisting the human during high-risk sub-tasks. However, using a robotic assistant requires transforming a purely human task into a sequence of safe and effective human-robot collaborative actions. Since diseases like Ebola can spread through the mucous membranes of the face our goal in synthesizing these actions is to keep the human's hands away from his or her face as much as possible. As a secondary goal, we also seek to minimize the human's effort. We segment the doffing procedure into a sequence of human and robot actions such that the robot only assists when necessary and the human performs the more intricate parts of the procedure. Our framework then synthesizes assistive motions for the robot that perform parts of the tasks. Our experiments on five doffing tasks suggest that the introduction of a robot assistant improves the safety of the procedure in three out of four of the high-risk doffing tasks while reducing effort in all five tasks.