I-Corps: Adaptive Robotic Nursing Assistants for Physical Healthcare Delivery

University Of Louisville Research Foundation Inc, Louisville KY

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of robotic technologies to assist healthcare workers deliver better and more efficient services to patients in hospitals and nursing home environments. The Bureau of Labor statistics indicates that there are millions of registered nurses in the United States, which makes them the largest workforce providing healthcare. The proposed technology has the potential to augment the nursing workforce and help offset personnel shortages in hospitals, long-term care facilities, and nursing homes. Adaptive robotic nursing assistants may reduce nurses’ exposure to infectious agents, reduce musculoskeletal injuries, and increase job satisfaction by freeing up time spent on item fetching, repetitive measurements of vital signs, and lifting. A robot that can communicate with the electronic health records to document completed tasks may have greater impact by saving nurses’ time devoted to paperwork. With the predicted shortage in nursing workforce, nurse workloads are expected to increase, and robot nursing assistants have the potential to handle some of the physical and administrative nursing tasks. Patients and hospitals may gain in terms of quality of care, prevention of falls, and faster service through automation. This I-Corps project is based on the development of adaptive robotic nursing assistants for physical and administrative tasks in hospital environments. The technology consists of a mobile manipulator with an omni-directional base, a 6-degrees of freedom (DOF) robotic arm, and sensors to assist with nursing scenarios such as sitting and walking with patients. The sensors include both non-contact proximity and contact tactile skins to allow the robot to operate safely in busy healthcare environments while interacting physically with nurses and patients. The technology also incorporates human-machine interfaces (HMIs) that allow users to interact with the robot in an intuitive manner that may be tailored to individual preferences. The proposed technology’s neuroadaptive learning control technology may adapt to each user’s behavior during physical human-robot interaction, ensuring both high quality interaction, programmable adaptive HMI behaviors, and robust performance under operational uncertainties. Using this technology, the robot can navigate the environment, fetch objects, and automate mundane nursing tasks while keeping healthcare workers in the decision loop. 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.