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Title: Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation
An estimated 11% of adults report experiencing some form of cognitive decline which may be associated with conditions such as stroke or dementia, and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment’s effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation, and identify key principles to working in this space. We then examine state-of-the-art technical approaches to enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will more » help guide the robotics community as they continue to provide more engaging, effective, and personalized interactions between people and robots. « less
Authors:
;
Award ID(s):
1915734
Publication Date:
NSF-PAR ID:
10285172
Journal Name:
Annual review of control robotics and autonomous systems
ISSN:
2573-5144
Sponsoring Org:
National Science Foundation
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