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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 OpenAlex
Marko Mikic, Peter Francis, Thomas Looi, J. Ted Gerstle, James M. Drake

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.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
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

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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