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Record W4220801845 · doi:10.1145/3490100.3516476

Integrating in-hand physical objects in mixed reality interactions

2022· article· en· W4220801845 on OpenAlexaff
Richard Nguyen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHuman–computer interactionComputer scienceGestureHeadsetMixed realityModalitiesVirtual realityAugmented realityContext (archaeology)MultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

With the launch of commercial mixed reality headsets, it has become increasingly important to find new interaction modalities making use of their capabilities. Indeed, endowed with spatial understanding, those devices offer the possibility to interact with the physical environments around the users and move beyond the traditional WIMP(Windows, Icons, Menus, Pointer) paradigm. In this context, this research project aims at designing and implementing interactions that are seamless for users who are using physical tools during assembling tasks while wearing a mixed reality headset that provides them instructions. The goals of this project are to provide natural interactions despite having tools in hand: 1) by considering the tools in the hands while recognizing gestures or by using them to directly interact with the simulated virtual physics, 2) by associating different computational features such as mapped functionalities or allowed interactions depending on the tool being used or its shape. To meet those goals, we are iterating over the implementation of prototypes involving object and gesture recognitions and will evaluate the designed interactions in an assembly scenario.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.299
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2022
Admission routes1
Has abstractyes

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