NSF Award
2026584
This Future of Work at the Human-Technology Frontier (FW-HTF) research project advances a vision for the profession of nursing where collaborative human-robot interfaces (CHRIs) can enhance nurse productivity and reduce on-the-job stress. Interfaces in this project are broadly defined as any link between humans and intelligent machines such as robots, using advanced sensors, mobile computing, and display devices. In this project, interfaces will intelligently ?adapt? to provide both physical and cognitive assistance to nurses and patients in future healthcare environments. The project will pursue four objectives. The team will develop a taxonomy of nursing tasks to determine those that can be justifiably delegated to intelligent robots. The team will then compare the ability of two novel CHRIs to facilitate stable and effective shared human-robot control of a robot-assisted walking task (i.e., fall prevention). They will also investigate a CHRI recommender system that coordinates patient sitting tasks such as vital signs monitor and item fetching among several nurses and mobile manipulators. The goal here is to determine the optimal number of robotic assistants per group of patients and nurses. Finally, the team will evaluate the social and economic impact of the technology on nurses, patients, and healthcare facilities. The project will promote the progress of science and advance the national health by providing a blueprint for engineering future nursing assistant robots, for informing healthcare facility design to accommodate the robots, and for advancing instruction on the use of intelligent robotic assistants into formal nursing education, nurse training, and credentialing. Other potential benefits of the project include the development of instructional programs in robotics and machine learning at the University of Louisville, involvement of undergraduate nursing students in this research, and outreach to rural primary care clinics and hospital settings through the Center for Health Systems Innovation at the Oklahoma State University.<br/><br/>This project includes four objectives: Behavioral observation, documentation reviews, task inventories and critical incidents will be analyzed to develop a task, skill, and context taxonomy to identify nursing tasks that can be assigned to intelligent robotic nurse assistants; The team will develop two CHRIs and then compare their performance in an assisted walking task with the abilities of human nursing staff. These interfaces utilize neural networks and generic algorithms and adjust to psycho-physiological and tactile signals from users and are designed to allow novice nurses and patients to operate robots with wearable sensors for prevention of falls. The team will then enhance the informational capabilities of the physical CHRIs using collaborative filtering, hybrid recommendation and machine learning techniques. The goal here is to develop an intelligent recommender system that will promote efficiency in the deployment of robotic assistants capable of performing patient sitting tasks such as vital signs monitoring and item fetching. The interfaces in this project will be evaluated by approximately 150 expert and novice users, nursing students and simulated patients to advance understanding of which types of tasks are better assigned to people and which may be delegated to robots in nursing scenarios. Finally, the team will perform economic analyses of the impact of the technology on nursing costs and the skilling needs for future healthcare industry through O*NET databases.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
