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Perspectives on Mobile Robots as Tools for Child Development and Pediatric Rehabilitation

2007· article· en· W2032764745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAssistive Technology · 2007
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsRobotHuman–computer interactionMobile robotComputer scienceVariety (cybernetics)Field (mathematics)RoboticsRehabilitationArtificial intelligenceMedicinePhysical therapy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.362
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it