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Record W2151726333 · doi:10.1109/iembs.2004.1403251

Center of mass function approximation

2005· article· en· W2151726333 on OpenAlexaff
Aimee L. Betker, Zahra Moussavi, Tony Szturm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceTrajectoryCenter of mass (relativistic)Function (biology)AccelerometerStability (learning theory)Genetic algorithmCenter (category theory)Mathematical optimizationControl theory (sociology)Control (management)Artificial intelligenceMathematicsMachine learning

Abstract

fetched live from OpenAlex

In this paper, a hybrid genetic algorithm sum-of-sines model is developed to estimate the resultant center of body mass (COM) trajectory. The COM is an important parameter to consider when evaluating or analyzing human postural control, and is indicative of the system's stability. However, currently available systems that calculate the COM are not readily available for clinical routine assessment, making it difficult to widely assess balance problems. The input to the genetic sum-of-sines model developed in this paper is acquired through two accelerometers; equipment that is inexpensive, easy to use and portable. The results indicate that the model developed in this paper shows promising results for obtaining COM estimates that have clinical applications.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.027
GPT teacher head0.340
Teacher spread0.313 · 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 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

Citations6
Published2005
Admission routes1
Has abstractyes

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