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Record W2001710420 · doi:10.1109/memea.2013.6549706

Correcting Smartphone orientation for accelerometer-based analysis

2013· article· en· W2001710420 on OpenAlex
Marco D. Tundo, Edward D. Lemaire, Natalie Baddour

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAccelerometerOrientation (vector space)QuaternionRotation matrixComputer scienceOffset (computer science)Position (finance)Rotation (mathematics)Computer visionFrame (networking)AccelerationGyroscopeReference frameArtificial intelligenceMathematicsEngineeringPhysicsGeometry

Abstract

fetched live from OpenAlex

A method was developed for rotating a Smartphone accelerometer coordinate system from an offset to a predetermined three-dimensional position to improve accelerometer-based activity identification. A quaternion-based rotation matrix was constructed from an axis-angle pair, produced via algebraic manipulations of the gravity acceleration components in the device's body-fixed frame of reference with the desired position of the vector. The rotation matrix is constructed during quiet standing and then applied to all subsequent accelerometer readings thereafter, transforming their values in this new fixed frame. This method provides a consistent accelerometer orientation between people, thereby reducing Smartphone orientation variability that can adversely affect activity classification algorithms.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.551

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)0.0010.000

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 head0.010
GPT teacher head0.215
Teacher spread0.204 · 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