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Record W4414161116 · doi:10.1145/3743707

Investigating Hand-Bound Pads for AR Input Using Hand-Tracking Only MHCI018

2025· article· en· W4414161116 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

VenueProceedings of the ACM on Human-Computer Interaction · 2025
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsBerger (Canada)
FundersAssociation Nationale de la Recherche et de la TechnologieAgence Nationale de la Recherche
KeywordsTouchpadTracking (education)Input deviceControl (management)Tracking system3D interaction

Abstract

fetched live from OpenAlex

Interaction in Augmented Reality primarily relies on raycast pointing and mid-air touch. An alternative consists of using the non-dominant hand as a touch-sensitive surface, enabling more comfortable, less fatiguing input. AR UI design guidelines have so far discouraged this alternative because of poor hand tracking performance when the hands overlap, favoring touchpads in the air near the hand, rather than on the hand. But significant improvements to the hand tracking capabilities of recent commodity headsets suggest that on-hand pads may now be feasible. We develop an on-hand touchpad prototype and conduct two studies that involve both discrete input and continuous control tasks. The first study compares such on-hand pads to baseline in-air and on-object pads, showing comparable performance despite some limitations in tracking accuracy. The second study quantifies the advantage of on-hand and in-air pads over on-object pads during transitions between touchpad input and other physical hand activities.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.918

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.089
GPT teacher head0.368
Teacher spread0.279 · 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