Assistant Robot Enhances the Perceived Communication Quality of People With Dementia: A Proof of Concept
Bibliographic record
Abstract
Almost all older people with dementia have progressive communication difficulties, which lead to increased social isolation and negative emotions. Thus, providing communication assistance for them is essential. This paper explores the feasibility of using social robots to assist older people with dementia in their face-to-face communication with others. We designed the behavior of a humanoid Pepper robot and made a Wizard of Oz prototype that the robot can serve as a personal memory assistant. The robot stores personal information for older people and assists in their communication through voice and screen display. In a video-based study with 88 participants, we investigated the effects of this assistive robot from a third-person observer perspective. Data were collected and analyzed using both three-way MANCOVAs for quantitative analysis and conventional content analysis for qualitative data. The results revealed that, by providing memory support, the robot significantly improved the observer's perceptions of an older person with dementia, including her perceived communication ability and performance, and personal image. Meanwhile, the communication is perceived to be significantly more effective when the robot assisted an older person. The willingness of others to communicate with more senior people also increased accordingly. Based on these findings, we present guidelines that may inform the design and development of communication assistant robots for older people with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".