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Record W3000822078 · doi:10.1080/10400435.2020.1719557

A feasibility study of eye gaze with biofeedback in a human-robot interface

2020· article· en· W3000822078 on OpenAlexafffund
Isao Sakamaki, Kim Adams, Mahdi Tavakoli, Sandra A. Wiebe

Bibliographic record

VenueAssistive Technology · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
FundersGlenrose Rehabilitation Hospital FoundationCIHR Skin Research Training CentreCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsGazeFixation (population genetics)Haptic technologyRobotAuditory feedbackHuman–computer interactionComputer scienceVirtual machineEye movementEye trackingInterface (matter)BiofeedbackModalitiesTask (project management)Virtual realityComputer visionSimulationArtificial intelligencePhysical medicine and rehabilitationEngineeringPsychologyMedicine

Abstract

fetched live from OpenAlex

Play is a vital activity in which children learn skills and explore the environment through object manipulation. Assistive robots have been used to provide access to play, and Forbidden Region Virtual Fixture (FRVF) guidance at the user interface could help the users make the robot traverse the play environment more efficiently because it behaves like virtual walls to follow. Eye gaze was used to indicate the user's intended target and generate the location of the virtual walls in a card sorting task. We eliminated the typical computer screen required for visual feedback to confirm gaze location, and examined the use of alternative feedback. In this feasibility study, first a group of adults without physical impairment tested the system with auditory and vibrotactile feedback modalities for the gaze fixation and with the virtual walls on and off for robot movement. Then case studies with children and individuals with physical impairments were performed. Even though gaze fixation feedback and the virtual wall did not improve the performance of adult participants without impairment, the feedback increased the speed and accuracy of the gaze fixation and the virtual walls improved the movement efficiency for the participants with impairment and a 6-year-old child without impairment.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.337
Teacher spread0.296 · 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.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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