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Record W4414114173 · doi:10.1145/3743708

ThumbSwype: Thumb-to-Finger Gesture Based Text-Entry for Head Mounted Displays MHCI031

2025· article· en· W4414114173 on OpenAlex
Rishav Banerjee, Shariff AM Faleel, Omang Baheti, Khalad Hasan, Pourang Irani

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
TopicHand Gesture Recognition Systems
Canadian institutionsL'Alliance BoviteqOkanagan University CollegeUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsSwIPeGestureText entryHead (geology)Mobile deviceAccelerometer

Abstract

fetched live from OpenAlex

Designing a comfortable, familiar, and efficient one-handed text entry method for Head-Mounted Displays (HMDs) remains a significant challenge. Existing midair typing systems induce fatigue, while novel techniques often demand extensive training or sacrifice input efficiency. Consequently, we introduce ThumbSwype , a novel thumb-to-finger text entry technique that adapts smartphone swipe typing for HMDs. Users see the traditional QWERTY keyboard overlaid on their index, middle, and ring fingers, allowing them to perform swipe gestures with their thumb to type words. In an evaluation study (N=16) , participants achieved a mean of 14.52 words per minute (WPM), which is 63.8% of their smartphone swipe-typing performance, with a peak average of 20.2 WPM. We compare ThumbSwype’s performance with related work, and discuss directions for future improvement.

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 score0.354
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.356
Teacher spread0.316 · 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