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Record W2979980913 · doi:10.1109/embc.2019.8857519

Bone Conduction Headphones for Force Feedback in Robotic Surgery

2019· article· en· W2979980913 on OpenAlexaff
Marko Mikic, Peter Francis, Thomas Looi, J. Ted Gerstle, James M. Drake

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsHeadphonesDistractionHaptic technologyBone conductionAuditory feedbackAudio feedbackComputer scienceVisual feedbackHuman–computer interactionSimulationComputer visionAudiologyMedicineAcousticsPsychologyPhysicsCognitive psychology

Abstract

fetched live from OpenAlex

Bone conduction headphones (Fig. 1) offer the unique ability to provide auditory information to the user without obstructing external sounds. We apply this technology to robotic surgery to provide the surgeon with force feedback information with minimal distraction. The device is evaluated by pairing it with a force sensor that is attached to a suture pad. Four participants were tasked to complete 25 sutures on the suture pad while either receiving no feedback or audio, visual, or combined feedback that represents the magnitude of their applied force. Trials performed with bone conducting headphones had noticeable improvements compared to previous trials without feedback, while the most noticeable improvements were observed for cases with both visual and auditory feedback. Auditory feedback may have an important role in a robotic surgery setting and bone conduction headphones may enable this form of feedback with minimal distraction.

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)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.002

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 head0.021
GPT teacher head0.227
Teacher spread0.206 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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