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Record W2107327156 · doi:10.1109/tbme.2010.2100394

Head and Trunk Segment Moments of Inertia Estimation Using Angular Momentum Technique: Validity and Sensitivity Analysis

2010· article· en· W2107327156 on OpenAlexaff
Mohsen Damavandi, Georgios A. Stylianides, Nader Farahpour, Paul Allard

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMoment of inertiaSensitivity (control systems)Head (geology)TrunkAngular momentumPhysicsComputer scienceClassical mechanicsEngineeringElectronic engineeringGeology

Abstract

fetched live from OpenAlex

Classical models to estimate the head and trunk (HT) moments of inertia (I) are limited to populations from which the anthropometric measures were obtained. The purposes of this study were to determine if the angular momentum technique can be used to estimate subject-specific HT's I values and test its validity and sensitivity. Twenty-three adults who participated in this study were divided into three morphological groups according to their body mass index (BMI). Using the proposed technique, the HT's I values were estimated for the whole sample and compared to three well-known methods to test its validity. The sensitivity of the proposed method was verified while applied to individuals with different BMI (i.e., lean, normal, and obese). The angular momentum technique gave I values within the range of those of the three methods for the entire sample. Statistical differences were identified between the lean and obese groups in relative radii of gyration for the anteroposterior and mediolateral axes ( P<0.05). Since the proposed technique makes no assumption on the mass distribution and segments' geometry, it appeared to be more sensitive to body morphology changes in estimating the HT's I values in lean and obese subjects compared to the classical methods.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.985
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.333
Teacher spread0.312 · 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 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

Citations10
Published2010
Admission routes1
Has abstractyes

Explore more

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