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Record W6926172234 · doi:10.20380/gi2021.36

Simulating Mass in Virtual Reality using Physically-Based Hand-Object Interactions with Vibration Feedback

2021· article· en· W6926172234 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

VenueCanada Human-Computer Communications Society · 2021
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHeadsetVirtual realityHaptic technologyVirtual imageSet (abstract data type)Object (grammar)VibrationTask (project management)

Abstract

fetched live from OpenAlex

Providing the sense of mass for virtual objects using un-grounded haptic interfaces has proven to be a complicated task in virtual reality. This paper proposes using a physically-based virtual hand and a complementary vibrotactile effect on the index fingertip to give the sensation of mass to objects in virtual reality. The vibrotactile feedback is proportional to the balanced forces acting on the virtual object and is modulated based on the object's velocity. For evaluating this method, we set an experiment in a virtual environment where participants wear a VR headset and attempt to pick up and move different virtual objects using a virtual physically-based force-controlled hand while a voice-coil actuator attached to their index fingertip provides the vibrotactile feedback. Our experiments indicate that the virtual hand and our vibration effect give the ability to discriminate and perceive the mass of virtual objects.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.961

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 studies0.0010.000
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 head0.045
GPT teacher head0.298
Teacher spread0.254 · 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