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Record W2126721563 · doi:10.1109/tnsre.2005.856075

School-based use of a robotic arm system by children with disabilities

2005· article· en· W2126721563 on OpenAlexaff
A. Cook, Bret G. Bentz, Norma Harbottle, Cheryl L. Lynch, B. L. Miller

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2005
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of TorontoUniversity of Alberta HospitalAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsPsychologyCompetence (human resources)RobotTask (project management)Goal Attainment ScalingComputer scienceRobotic armHuman–computer interactionArtificial intelligenceEngineeringRehabilitationSocial psychology

Abstract

fetched live from OpenAlex

A robotic arm system was developed for use by children who had very severe motor disabilities and varying levels of cognitive and language skills. The children used the robot in a three-task sequence routine to dig objects from a tub of dry macaroni. The robotic system was used in the child's school for 12-15 sessions over a period of four weeks. Goal attainment scaling indicated improvement in all children in operational competence of the robot, and varying levels of gain in functional skill development with the robot and in carryover to the classroom from the robot experiments. Teacher interviews revealed gains in classroom participation, expressive language (vocalizations, symbolic communication), and a high degree of interest by the children in the robot tasks. The teachers also recommended that the robot should have more color, contrast and character, as well as generating sounds and/or music for student cues. They also felt that the robotic system accuracy should be increased so that teacher assistance is not necessary to complete the task.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.026
GPT teacher head0.299
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations76
Published2005
Admission routes1
Has abstractyes

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