Machine Learning Assist in Remote Operation of Nursing Mobile Robot

This paper presents a comprehensive study on the integration and effects of machine learning assist in the remote operation of a nursing mobile robot, a crucial tool in the evolving landscape of healthcare technology. We begin by outlining the growing need for advanced robotic assistance in nursing, driven by challenges such as an aging population and a global shortfall in healthcare staff. The study is divided into two main sections: simulation-based research and experimental trials. In the simulation-based research, we develop and test a machine learning assist algorithm within a virtual environment, aimed at improving the precision and safety of remote nursing robot operations. This section includes an analysis of the robot's performance in tasks requiring delicate manipulation, like ad-ministering medication. The experimental trials are conducted using a prototype nursing robot equipped with machine learning assisted controller. These trials involve real-world scenarios, such as navigating through hospital corridors and performing basic nursing tasks. The effectiveness of machine learning assist algorithm could be evaluated in terms of operator accuracy in obstacle avoidance. Our results demonstrate that incorporating machine learning assist significantly enhances the efficiency and safety of remote nursing robot operations. Operators reported improved control and greater confidence in performing delicate tasks, leading to a higher standard of patient care.