Enhancing Relaxation Through Co-Design with People Living with Dementia: The CALM Robot
Bibliographic record
Abstract
Abstract The involvement of individuals living with dementia in the co-design of assistive technologies is crucial to ensuring that these innovations meet their needs, preferences, and lived experiences. This study presents insights from a co-design process involving 4 people living with dementia (PLwD), 6 caregivers, and 2 healthcare professionals to develop a social robot designed to facilitate relaxation and deep-breathing exercises. The research employed a co-design framework, integrating PLwD as active participants in design workshops, prototype evaluations, and reflection meetings. Qualitative data were collected through structured discussions, observation notes, and participant feedback. The iterative design process allowed for continuous refinement of the robot’s features, focusing on tactile engagement, intuitive interactions, and non-verbal communication. Findings highlight the importance of structured workshop discussions, the use of visual aids, and the creation of an inclusive environment to facilitate participation by PLwD. Participants reported that the robot provided a calming experience and demonstrated potential for use in care settings. However, some PLwD expressed hesitation in sharing opinions when caregivers were present, suggesting the need for separate workshops to foster uninhibited feedback. This study provides a model for integrating intended users into the design process, offering insights for researchers developing assistive technologies in dementia care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".