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Initial Exploration into Electrotactile Tongue Stimulation for Providing Force Feedback for Robot-Assisted Surgery

2024· article· en· W4405271088 on OpenAlexaff
Dinmukhammed Mukashev, Agnieszka Lach, Chet W. Hammill, Zhanat Kappassov, Adwait Sharma, Aditya Shekhar Nittala, Luv Kohli, Sharif Razzaque

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRobotComputer scienceTongueStimulationHaptic technologyMedical roboticsHuman–computer interactionSurgeryMedicineSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

We report findings from initial exploration into using electrotactile stimulation, on the surgeon's tongue, as a potential lower-latency and less mechanically-complex way to provide force-feedback to the operator of robot-assisted surgery. We conducted a pilot feasibility study wherein participants attempted to teleoperate a robot to grasp and lift chicken eggs without breaking or dropping them. The force measured by the robot's gripper was displayed differently based on the experimental condition: visually only, or visually with electrotactile tongue stimulation. Participants were more successful lifting eggs with tongue stimulation. Data from this preliminary study, along with insights from informal interviews, suggest that tongue stimulation has potential to enhance the efficacy and safety of robot-assisted surgery.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.360
Teacher spread0.299 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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