Stature Estimation from Foot Measurements in Primary Students: A Case Study in Bangladesh
Bibliographic record
Abstract
This study investigates the relationship between foot anthropometric measurements and human stature among primary school students in Bangladesh, aiming to establish reliable anthropometric models for forensic and ergonomic applications. Employing data from 360 children (180 males and 180 females), the study utilizes linear and multiple regression models to establish predictive equations for stature estimation. Foot measurements, including foot length and width for both feet, were recorded with high precision. Results reveal strong correlations between foot dimensions and stature, with foot lengths showing higher predictive accuracy for males, and foot widths being more effective for females. Multiple regression analysis enhances predictive precision, with gender-specific models significantly improving accuracy. The female-specific multiple regression model demonstrated an almost perfect prediction correlation (R = 0.9997), compared to the male model (R = 0.9524). The findings underscore the necessity of using gender-specific equations due to variations in growth patterns and body proportions. This research fills a critical gap in anthropometric studies focused on children and presents practical applications for forensic science and ergonomic design in resource-limited settings. Additionally, it contributes to the standardization of foot dimensions based stature estimation techniques, enhancing their applicability for diverse demographic groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".