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Record W3138763178

Wearable sensor performance for clinical motion tracking of the lumbar spine

2019· article· en· W3138763178 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

VenueCMBES Proceedings · 2019
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsRange of motionInertial measurement unitMotion captureCorrelation coefficientTracking (education)Motion analysisMathematicsMotion (physics)OrthodonticsComputer scienceArtificial intelligenceMedicinePhysical therapyStatisticsPsychology
DOInot available

Abstract

fetched live from OpenAlex

Inertial measurement units (IMUs) have potential to be integrated into clinical assessments of movement-related disorders of the spine. This study evaluated 2 Mbientlab MetaMotionR IMUs relative to Vicon motion capture equipment in tracking 3D spine motion during 35 cycles of constrained repetitive spine flexion-extension (FE) in 10 participants. Root-mean-square error (RMSE) was low in all anatomical planes (RMSE ≤ 2.43°). Pearson’s correlation coefficient was strong in the FE and lateral bend (LB) planes (R ≥ 0.746), and weak-to-moderate in the axial twist (AT) plane (0.343 ≤ R ≤ 0.679). Additionally, there was very strong correlation between range of motion measurements in the FE plane (ICC2,1 = 0.99), and a wide range from weak to strong in the LB and AT planes (0.239 ≤ ICC2,1 ≤ 0.980). This study reveals that the IMUs perform well in tracking motion in the primary movement plane, and can be used for planar assessments of movement quality.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.345
Teacher spread0.285 · 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