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Record W4308796730 · doi:10.5772/intechopen.108330

Improving Medical Simulation Using Virtual Reality Augmented by Haptic Proxy

2022· book-chapter· en· W4308796730 on OpenAlex

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

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHaptic technologyVirtual realityComputer scienceProxy (statistics)Immersion (mathematics)Human–computer interactionPerceptionSimulationArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

This chapter explores how the realism of haptic perception in virtual reality can be significantly enhanced with the help of the concept of haptic proxy. In haptic proxy, the position and orientation of physical objects are tracked in real-time and registered to their virtual counterparts. A compelling sense of tactile immersion can be achieved if the tracked objects have similar tactile properties to their virtual counterpart. A haptic proxy prototype was developed, and a pilot study was conducted to determine if the haptic proxy system is more credible than standard virtual reality. To test our prototype, we performed simple medical tasks such as moving a patient’s arm and aiming a syringe to specific locations. Our results suggest that simulation using a haptic proxy system is more believable and user-friendly and can be extended to developing new generations of open surgery simulators.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.310
Teacher spread0.265 · 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