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Record W2404236138 · doi:10.1109/taffc.2015.2457893

Design and Evaluation of a Touch-Centered Calming Interaction with a Social Robot

2015· article· en· W2404236138 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Affective Computing · 2015
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHuman–computer interactionRobotHuman–robot interactionComputer scienceRoboticsSocial robotBreathingRehabilitation roboticsComponent (thermodynamics)PsychologyArtificial intelligenceRobot controlMobile robot

Abstract

fetched live from OpenAlex

With advances in sensor and actuator design, intelligent computing techniques and personal care robotics, today's robots hold promise as fully interactive, therapeutic human companions. To achieve this ambitious goal, key interaction components must be identified and then systematically designed and evaluated. Based on successes of human-animal therapy, we propose affective touch as one such component. Delivering this adjunct in a controllable robot form allows us to examine its efficacy for therapeutic applications such as anxiety management. With an approach grounded in social cognitive theories for human-animal relations, we deployed a social robot, the Haptic Creature, in an interaction designed to be calming: participants held the robot on their laps and stroked it as it was breathing. As a result, their heart and respiration rates significantly decreased relative to stroking a non-breathing robot. They also reported themselves as calmer and happier.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.712
Threshold uncertainty score0.621

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)0.0000.000

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 head0.183
GPT teacher head0.417
Teacher spread0.234 · 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