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Yuan Gao 0066

dblp:76/2452-66 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0006-4861-5313ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 38% Health and well-being technologies · 38% Accessibility and assistive technology · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies
mild cognitive impairment
0.912025
Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment · HRI 2025
Human-robot interaction › assistive robotics
socially assistive robotics
0.912025
Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment · HRI 2025
Accessibility and assistive technology
older adults
0.312025
Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment · HRI 2025
Human-AI interaction › virtual agents
virtual human
0.312025
Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment · HRI 2025

Methods — techniques the papers use, named apart from their topics

co-design · 0.9
YearPublicationVenuePosition
2025 Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment
abstract
Mild cognitive impairment (MCI) is an early stage of cognitive decline that significantly increases the risk of dementia, making early interventions crucial for maintaining cognitive health in older adults. Our research takes a co-design approach to understand, design, and evaluate how socially assistive robots and a virtual human can promote lifestyle changes for people with MCI (pwMCI), potentially improving their cognitive health. Through an iterative refinement process, we aim to develop adaptable, user-friendly, and sustainable technologies that foster long-term engagement across diverse settings. Ultimately, our goal is to improve cognitive health, quality of life, emotional well-being, and loneliness in pwMCI.
Yuan Gao 0066, Ngaire Kerse, Bruce A. MacDonald, Elizabeth Broadbent
HRI1