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Record W4408727277 · doi:10.1109/tvcg.2025.3549869

PantographHaptics: A Technique for Large-Surface Passive Haptic Interactions using Pantograph Mechanisms

2025· article· en· W4408727277 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

VenueIEEE Transactions on Visualization and Computer Graphics · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of CalgaryUniversity of WaterlooIntertek (Canada)
Fundersnot available
KeywordsComputer scienceHaptic technologyPantographHuman–computer interactionVisualizationSimulationComputer graphics (images)Artificial intelligenceEngineeringEngineering drawing

Abstract

fetched live from OpenAlex

In Virtual Reality (VR), existing hand-scale passive interaction techniques are unsuitable for continuous large-scale renders: room-scale proxies lack portability, and wearable robotic arms are energy-intensive and induce friction. This paper presents a technique for providing wall haptics in VR which supports portable, passive, and large-scale user interactions. We propose a potential solution, PantographHaptics, a technique which uses the scaling properties of a pantograph to passively render two-degree-of-freedom body-scale surfaces to overcome the limitations present in existing methods. We demonstrate PantographHaptics through two prototypes: HapticLever, a grounded system, and Feedbackpack, a wearable device. We evaluate these prototypes with technical and user evaluations. Our 9-participant first study compares HapticLever against traditional haptic modalities, while our 7-participant second study verifies Feedbackpack's usability and interaction fidelity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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