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Record W1986624256 · doi:10.1115/1.4000109

Accuracy of Inertial Motion Sensors in Static, Quasistatic, and Complex Dynamic Motion

2009· article· en· W1986624256 on OpenAlexafffund
Alison Godwin, Michael J. Agnew, Joan M. Stevenson

Bibliographic record

VenueJournal of Biomechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsQueen's UniversityLaurentian University
FundersWorkplace Safety and Insurance Board
KeywordsQuasistatic processPhysicsOrientation (vector space)Motion (physics)Inertial frame of referenceRange (aeronautics)PlanarInertial measurement unitAcousticsComputer scienceMathematicsComputer visionClassical mechanicsEngineeringGeometryComputer graphics (images)

Abstract

fetched live from OpenAlex

Inertial motion sensors (IMSs) combine three sensors to produce a reportedly stable and accurate orientation estimate in three dimensions. Although accuracy has been reported within the range of 2 deg of error by manufacturers, the sensors are rarely tested in the challenging motion present in human motion. Their accuracy was tested in static, quasistatic, and dynamic situations against gold-standard Vicon camera data. It was found that static and quasistatic rms error was even less than manufacturers' technical specifications. Quasistatic rms error was minimal at 0.3 deg (+/-0.15 deg SD) on the roll axis, 0.29 deg (+/-0.20 deg SD) on the pitch axis, and 0.73 deg (+/-0.81 deg SD) on the yaw axis. The dynamic rms error was between 1.9 deg and 3.5 deg on the main axes of motion but it increased considerably on off-axis during planar pendulum motion. Complex arm motion in the forward reaching plane proved to be a greater challenge for the sensors to track but results are arguably better than previously reported studies considering the large range of motion used.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations105
Published2009
Admission routes2
Has abstractyes

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