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A kinematics recommendation for trunk stability and control assessments during unstable sitting

2019· article· en· W2972139578 on OpenAlexafffund
Brad Roberts, Albert H. Vette

Bibliographic record

VenueMedical Engineering & Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsGlenrose Rehabilitation HospitalAlberta Health ServicesUniversity of Alberta HospitalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinematicsTrunkSittingPelvisPhysical medicine and rehabilitationSpeed wobbleWork (physics)Motion (physics)Kinematic chainComputer scienceSimulationMathematicsPhysicsMedicineAnatomyArtificial intelligenceBiologyClassical mechanics

Abstract

fetched live from OpenAlex

Trunk control has been commonly studied via an unstable sitting paradigm, with the majority of analyses using angular kinematics-based, posturographic measures. However, considerable variability in the choice of kinematics exists. Furthermore, the kinematics capturing the completion of this task are unknown. The purpose of this study was to characterize the kinematics in unstable sitting by quantifying and comparing the angular motion of the base of support, pelvis, and trunk as elicited via a commonly used wobble board (WB) paradigm. WB, pelvis, and trunk motion was recorded in fifteen non-disabled participants sitting on a wobble board. Posturographic measures were calculated and compared between corresponding WB and pelvis, and between WB and trunk angles. The trunk was stabilized through relatively large WB motion, with the trunk adopting a quasi-static pose. For all measures, angles, and conditions, the WB measure values were significantly larger than their corresponding pelvis or trunk values. Our findings demonstrate that the WB-human system is stabilized by regulating WB motion. Future work utilizing an unstable sitting surface and kinematics-based analyses to investigate trunk control should include the analysis of base of support kinematics.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations10
Published2019
Admission routes2
Has abstractyes

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