This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project aims to provide next-generation assistive robots to support the activities of hospital-based registered nurses (RNs). There are nearly three million registered nurses employed in the United States, making them the largest pool of healthcare providers in the country. Technology that affects the performance of this large labor pool cannot fail to have impact. Due to advancements in robotics and computer technology, access to intelligent communication, sensing, and computing hardware is on the cusp of becoming common--not only for healthcare professionals, but also for patients themselves. The project led by The University of Kentucky at Louisville in collaboration with the University of Texas at Arlington will focus on the creation of new design tools that can configure the hardware and software of adaptive robotic nursing assistants (ARNA). ARNA will be specifically designed to assist nurses in healthcare facilities with simple tasks such as, lift assistance, delivery of everyday lightweight objects (medicine, medical wearable equipment), and some physical assistance with movement of heavier objects, such as furniture, gurneys, and the patients themselves. The design and engineering innovations resulting from insights gained in this project may have great value deployed as products in broader consumer markets in addition to hospitals. Examples include in-home service and assistive robots, robots for assistance in public venues, and co-Robot manufacturing where humans are in close proximity to robot workers. The improved understanding of human-robot and nurse-robot interaction could represent enabling technology that will facilitate research breakthroughs and increase productivity and social acceptance of robotics. The research will also advance the understanding of the perceptual effects of robot design aesthetics and interfaces.

The proposed Adaptive Robotic Nurse Assistants will navigate cluttered hospitals, while equipped with multi-modal skin sensors that can anticipate nurse intent, automate mundane low-level tasks, but keep nurses in the decision loop. Modular and strong hardware will be deployed in reconfigurable platforms specially designed for nurse physical assistance. Adaptive human-machine interfaces will play a key role in this project, as these interfaces directly impact the ability of robots to help nurses in a dynamic, unstructured environment. Rather than pre-programming robot behaviors, learning algorithms will be used so that robots adapt to human preferences. Two leading applications are envisioned for feasibility evaluation by quantitative and qualitative metrics, including patient sitters and walkers. The sitter robot will take vital sign measurements, evaluate risk from patient movement and pose, and provide continuous observation of patients and feedback to and from nurses. The walker robot will assist nurses and patients by providing partial balance support, navigating cluttered environments, and assisting with medical equipment transportation.

The lead institution is the University of Kentucky at Louisville in collaboration with the University of Texas at Arlington with its multidisciplinary departments including the College of Engineering, College of Nursing, and the University of Texas at Arlington Research Institute (UTARI). Primary industrial partners include QinetiQ-North America (Waltham, MA), a large corporation specializing in unmanned systems, and RE2 (Pittsburgh, PA), a small business specializing in modular robotic manipulators that will contribute unique battle-tested hardware and systems engineering. In-hospital testing and evaluation of the proposed robots will be carried out by nurse researchers at the University of Texas at Arlington College of Nursing and Texas Health Resources (Dallas-Fort Worth, TX), a large healthcare provider.

Agency
National Science Foundation (NSF)
Institute
Division of Industrial Innovation and Partnerships (IIP)
Type
Standard Grant (Standard)
Application #
1643989
Program Officer
Jesus Soriano Molla
Project Start
Project End
Budget Start
2016-06-01
Budget End
2019-07-31
Support Year
Fiscal Year
2016
Total Cost
$880,776
Indirect Cost
Name
University of Louisville Research Foundation Inc
Department
Type
DUNS #
City
Louisville
State
KY
Country
United States
Zip Code
40202