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Record W2072404679 · doi:10.1016/j.procs.2013.06.061

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

2013· article· en· W2072404679 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

VenueProcedia Computer Science · 2013
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsComputer scienceAccelerometerGestureTracking (education)Mobile phoneComputer visionArtificial intelligenceMovement (music)Gesture recognitionFoot (prosody)Position (finance)PhoneHuman–computer interactionAcousticsTelecommunications

Abstract

fetched live from OpenAlex

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.067
GPT teacher head0.267
Teacher spread0.200 · 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