Perspectives on Mobile Robots as Tools for Child Development and Pediatric Rehabilitation
Bibliographic record
Abstract
Mobile robots (i.e., robots capable of translational movements) can be designed to become interesting tools for child development studies and pediatric rehabilitation. In this article, the authors present two of their projects that involve mobile robots interacting with children: One is a spherical robot deployed in a variety of contexts, and the other is mobile robots used as pedagogical tools for children with pervasive developmental disorders. Locomotion capability appears to be key in creating meaningful and sustained interactions with children: Intentional and purposeful motion is an implicit appealing factor in obtaining children's attention and engaging them in interaction and learning. Both of these projects started with robotic objectives but are revealed to be rich sources of interdisciplinary collaborations in the field of assistive technology. This article presents perspectives on how mobile robots can be designed to address the requirements of child-robot interactions and studies. The authors also argue that mobile robot technology can be a useful tool in rehabilitation engineering, reaching its full potential through strong collaborations between roboticists and pediatric specialists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".