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Record W2557343052 · doi:10.1109/iemcon.2016.7746294

Development of a graphical user interface for a socially interactive robot: A case study evaluation

2016· article· en· W2557343052 on OpenAlexaffabout
Vikram Banthia, Yaser Maddahi, Morgan May, David Blakley, Z Marzan Chang, Amanda Gbur, Chi Tu, Nariman Sepehri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGraphical user interfaceRobotInterface (matter)Human–computer interactionHumanoid robotAutismTest (biology)User interfaceComputer sciencePsychologyHuman–robot interactionDevelopmental psychologyApplied psychologySimulationArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we present the development of a graphical user interface for a commercially available humanoid robot to study its interaction with children. The interface has been designed to facilitate the overall operation of the robot, and ultimately improve the interaction with children. Four pilot cases are studied to investigate overall effectiveness of the developed robotic setup in interacting and educating children. Case studies included communication with children in university environment, a day nursery, a junior high school, and at the Winnipeg Children's Hospital. Children and their parents/staffs were asked to provide feedback on how they/their children felt about interacting with the robot. Responses were then translated into quantitative measures. The measures received by each group of children and parents/staffs were compared using the ANOVA test. Results failed to reject the null hypothesis of equality in means of all measures. Therefore, the feedback, provided by both groups using questionnaires, was considered very close to each other. In other words, results indicated that children and parents/staffs expressed great interest in use of the developed system, and believed that such a robot could be a helpful tool in child therapy like autism.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0040.000

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 head0.134
GPT teacher head0.492
Teacher spread0.358 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations7
Published2016
Admission routes2
Has abstractyes

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