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Record W2121317799 · doi:10.1109/whc.2005.123

Tactile Feedback Laser System with Applications to Robotic Surgery

2005· article· en· W2121317799 on OpenAlexaff
Peter Rizun, Garnette R. Sutherland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHaptic technologyLaserRoboticsComputer scienceTeleoperationImpressionOperator (biology)Point (geometry)Artificial intelligenceLaser surgeryTeleroboticsRobotComputer visionHuman–computer interactionOpticsPhysicsMathematicsMobile robot

Abstract

fetched live from OpenAlex

Despite the potential advantages of lasers, their adoption by surgeons has been limited. Unlike hand tools, lasers do not provide haptic feedback. With advances in augmented reality, haptics, and surgical robotics, it may now be possible to add the sense of touch to non-contact surgical lasers. This manuscript presents our initial work towards developing such a tactile feedback laser system. Two prototypes were constructed that allow an operator to feel surfaces using only light but are not yet equipped with lasers powerful enough to cut. Based on optical distance measurements, the prototypes synthesize haptic feedback through a robotic arm held by the operator when the focal point of the laser is coincident with a real surface, giving the operator the impression of touching something solid. To the best of our knowledge, no other device has been built that provides the sense of touch with a laser.

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 categoriesInsufficient payload (model declined to judge)
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 score0.961
Threshold uncertainty score0.998

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)0.0000.002

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 head0.013
GPT teacher head0.201
Teacher spread0.189 · 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.

Study designSimulation or modeling
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

Citations10
Published2005
Admission routes1
Has abstractyes

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