Human Robot Engagement and Acceptability in Residential Aged Care
Abstract
People with dementia often suffer the limitation in their ability to communicate and express themselves clearly, consequently spend most of their time alone, and are not engaged in social activity as before. The prolonged lack of engagement has contributed to a variety of negative health effects. The research on engagement and acceptability of people with dementia using assistive technology such as social robots can be useful to provide potential solutions and sustainability. Thus, this article focuses on the service design and the effectiveness of the engagement and acceptability while interacting with a social robot named Matilda being trialed from 2010 until 2013 with 115 participants in Australian residential aged care facilities. Combining the most updated engagement assessment method and the robot acceptance model as the methodology, the findings of this study indicate there is a statistically significant improvement in emotional, visual, and behavioral engagement of older people with social robots over the years. The post-trial survey has also verified their acceptance in the interaction with social robots.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PaPeRo could understand natural speech, but no specific details were given about its capacity for conducting autonomous, meaningful human-robot conversations.
“The robot could understand natural speech, but no specific details were given about its capacity for conducting autonomous, meaningful human-robot conversations.”
From Conversational Affective Social Robots for Ageing and Dementia Support (2022) · cites this paper as [51]
A longitudinal study with 115 people with dementia in Australian care residences and subsequent trials in five home settings showed significant improvements in emotional, visual, and behavioral engagement with PaPeRo.
“A longitudinal study on engagement and acceptance by 115 PwD living in Australian care residences [51] and subsequent trials in five home settings [82] showed significant improvements in emotional, visual, and behavioral engagement.”
From Conversational Affective Social Robots for Ageing and Dementia Support (2022) · cites this paper as [51]
Twelve studies presented results on IAT that were both accepted and used by the participants.
“Twelve studies presented results on IAT that were both accepted and used by the participants [19–21, 23, 27, 30–33, 42, 43, 45].”
From Acceptance and use of innovative assistive technologies among people with cognitive impairment and their caregivers: a systematic review (2019) · cites this paper as [31]
The citation lists haven’t been fetched yet
References 53 · Cited by 124
- Authors
- Khosla, R., Nguyen, K., & Chu, M.-T.
- Affiliations
- La Trobe University (AU)
- Corresponding author
- Khosla, Rajiv
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2016-12-27
- Volume
- 33
- Issue
- 6
- Pages
- 510-522
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W2564685734
- Cited by
- 124
- References
- 53
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 8 · 2023: 16 · 2022: 20 · 2021: 23 · 2020: 16 · 2019: 10 · 2018: 1 · 2017: 1
- FWCI
- 6.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- social robots, dementia, nursing homes, human-robot interaction, engagement, robot acceptance, service design, older adults, assistive technology, engagement assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review