An analysis of design recommendations for socially assistive robot helpers for effective human-robot interactions in senior care
Why this work is in the frame
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Bibliographic record
Abstract
As the global population ages, there is an increase in demand for assistive technologies that can alleviate the stresses on healthcare systems. The growing field of socially assistive robotics (SARs) offers unique solutions that are interactive, engaging, and adaptable to different users' needs. Crucial to having positive human-robot interaction (HRI) experiences in senior care settings is the overall design of the robot, considering the unique challenges and opportunities that come with novice users. This paper presents a novel study that explores the effect of SAR design on HRI in senior care through a results-oriented analysis of the literature. We provide key design recommendations to ensure inclusion for a diverse set of users. Open challenges of considering user preferences during design, creating adaptive behaviors, and developing intelligent autonomy are discussed in detail. SAR features of appearance and interaction mode along with SAR frameworks for perception and intelligence are explored to evaluate individual developments using metrics such as trust, acceptance, and intent to use. Drawing from a diverse set of features, SAR frameworks, and HRI studies, the discussion highlights robot characteristics of greatest influence in promoting wellbeing and aging-in-place of older adults and generates design recommendations that are important for future development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it