Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
Much research in healthcare robotics explores ex- tending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.
The emergency department (ED) is a safety-critical environ- ment in which mistakes can be deadly and providers are over- burdened. Well-designed and contextualized robots could be an asset in the ED by relieving providers of non-value added tasks and enabling them to spend more time on patient care. To support future work in this application domain, in this paper, we characterize ED staff workflow and patient experience, and identify key considerations for robots in the ED, including safety, physical and behavioral attributes, usability, and training. Then, we discuss the task representation and data needed to situate the robot in the ED, based on this do- main knowledge. To the best of our knowledge, this is the first work on robot design for the ED that explicitly takes task acu- ity into account. This is an exciting area of research and we hope our work inspires further exploration into this problem domain.