What question did this study set out to answer?

This study explores using footprint analysis for determining sex and reconstructing body proportions. It aims to evaluate predictive modeling based on footprint metrics.

July 8, 2026DiagnosticsOpen Access

Footprints as Morphometric Evidence for Somatic Prediction and Body Proportion Reconstruction in Forensic Medicine

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FAFatma Çam AygünMinistry of Justice SBSerdar BabacanBursa Uludağ Üni̇versi̇tesi̇TYTuğçe Koca YavuzMinistry of Justice

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Overview

Randomized trial applies footprints to estimate sex and body proportions, suggesting potential forensic applications.

Key Points

This study explores using footprint analysis for determining sex and reconstructing body proportions. It aims to evaluate predictive modeling based on footprint metrics.
Static bilateral footprints were collected via charcoal powder impressions and digitized with ImageJ.
Eleven footprint parameters and body measurements were recorded and analyzed using discriminant function analysis and regression models.
Models were validated using techniques like leave-one-out and 10-fold cross-validation.
The discriminant classification accuracy was 74.0% for the right foot and 71.0% for the left foot, decreasing to 64–67% post-validation.
Reduced regression models showed stable performance for estimating stature and arm span but demonstrated weak R2 values.

Cite This Study

Aygün et al. (2026) studied this question.

synapsesocial.com/papers/6a4de9e4d2ea289ef6283ed9 https://doi.org/10.3390/diagnostics16132114

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July 8, 2026DiagnosticsOpen Access

Footprints as Morphometric Evidence for Somatic Prediction and Body Proportion Reconstruction in Forensic Medicine

View Full Paper

Ask AI

Bookmark

Authors

FAFatma Çam AygünMinistry of Justice SBSerdar BabacanBursa Uludağ Üni̇versi̇tesi̇TYTuğçe Koca YavuzMinistry of Justice

Discussion

Member takes

Overview

Randomized trial applies footprints to estimate sex and body proportions, suggesting potential forensic applications.

Key Points

This study explores using footprint analysis for determining sex and reconstructing body proportions. It aims to evaluate predictive modeling based on footprint metrics.
Static bilateral footprints were collected via charcoal powder impressions and digitized with ImageJ.
Eleven footprint parameters and body measurements were recorded and analyzed using discriminant function analysis and regression models.
Models were validated using techniques like leave-one-out and 10-fold cross-validation.
The discriminant classification accuracy was 74.0% for the right foot and 71.0% for the left foot, decreasing to 64–67% post-validation.
Reduced regression models showed stable performance for estimating stature and arm span but demonstrated weak R2 values.

Cite This Study

Aygün et al. (2026) studied this question.

synapsesocial.com/papers/6a4de9e4d2ea289ef6283ed9 https://doi.org/10.3390/diagnostics16132114

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