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Record W4402438648 · doi:10.11159/icmie24.102

Analysis Of Improvements In The Practice Of Anthropometry Through 3D Scanning And Photogrammetry At UNITEC

2024· article· en· W4402438648 on OpenAlexvenueno aff
Tyzon Javier García Flores, Paola Michelle Pascua Cantarero

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety in Workplaces
Canadian institutionsnot available
Fundersnot available
KeywordsPhotogrammetry3d scanningAnthropometryComputer scienceComputer visionArtificial intelligenceComputer graphics (images)GeographyArchaeology

Abstract

fetched live from OpenAlex

At UNITEC Tegucigalpa, it was observed that, during anthropometry laboratory practices, body measurements were obtained from the subjects measured using traditional methods, where the measurers took the measurements in various ways.This variability caused fatigue in both the students being measured and the measurers, resulting in delays in obtaining results.Consequently, these results could have some degree of alteration, which was not conducive to the classroom activity.The objective was to analyze improvements in the methodology for taking anthropometric measurements through alternative measurement methods, with the participation of 14 students from the Methods Engineering I class.It was found that the photogrammetry method offered greater reliability (AE = -0.40cm) compared to the 3D scanning method (AE = +4.19cm).The photogrammetry method had the highest percentage of activity by the measuring student, reaching 100%, and reduced the time by 32.16% (average percentage relative error with AE = -8.13min) compared to the traditional method.On the other hand, the 3D scanning method resulted in a greater idle time for the measurer, accounting for 78.82% (7.79 minutes) of the total time.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.351
Teacher spread0.335 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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