In-person vs Remote HRI: A Comparative Study of Robot Facilitated Dance with Older Adults in Long-term Care
Bibliographic record
Abstract
As the global older population increases, a number of older adults need assistance in their daily lives. Social robots can be used to provide support for a number of activities including facilitating dance sessions. Research in this field has mainly considered physically embodied robots collocated in the same environment with the users. However, the experience of older adults with different robot presence conditions has not yet been explored. Robot presence can play an important role in investigating social human-robot interactions (HRI) with this vulnerable population. In this paper, we present a novel preliminary HRI study that investigates and compares how older adults' interaction behaviors vary during dance sessions facilitated by social humanoid robots in both in-person HRI and remote HRI conditions. Our study was conducted for the duration of a week with residents living in a long-term care home. Participation rates were higher in the in-person condition. However, caregiver questionnaire results found no statistically significant difference in engagement and enjoyment of the older residents between the two robot presence conditions. The caregivers observed the residents engaged and enjoying dancing with both the in-person and remote robot during the dance sessions. Our study is the first to show the potential of using remote social HRI to provide interventions to older adults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".