## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Intervention** & **ICC 95\% CI** & **\% Within 1 cm** & **SEM (cm)** & **CV\%** & **Bland–Altman** \\ \hline Belly & 0.918 [0.861; 0.952] & 75.5\% & 3.655 & 6.22 & \(-1.199\) [\(-11.330\); 8.932], 1 \\ Hip & 0.997 [0.995; 0.998] & 88.7\% & 0.517 & 0.76 & \(-0.132\) [\(-1.565\); 1.302], 4 \\ Thigh, dx & 0.997 [0.995; 0.998] & 96.2\% & 0.218 & 0.61 & \(-0.069\) [\(-0.673\); 0.535], 2 \\ Thigh, sx & 0.997 [0.995; 0.998] & 98.1\% & 0.223 & 0.62 & \(-0.024\) [\(-0.642\); 0.594], 2 \\ Knee, dx & 0.995 [0.992; 0.997] & 98.1\% & 0.212 & 0.80 & \(-0.046\) [\(-0.635\); 0.542], 3 \\ Knee, sx & 0.990 [0.983; 0.994] & 92.5\% & 0.316 & 1.19 & \(-0.056\) [\(-0.931\); 0.819], 6 \\ Calf, dx & 0.994 [0.990; 0.997] & 98.1\% & 0.213 & 0.84 & 0.087 [\(-0.503\); 0.677], 4 \\ Calf, sx & 0.991 [0.981; 0.995] & 96.2\% & 0.235 & 0.92 & 0.154 [\(-0.498\); 0.806], 2 \\ Ankle, dx & 0.974 [0.953; 0.985] & 98.1\% & 0.291 & 1.67 & 0.132 [\(-0.674\); 0.937], 2 \\ Ankle, sx & 0.956 [0.913; 0.976] & 92.5\% & 0.341 & 1.97 & 0.207 [\(-0.738\); 1.152], 1 \\ \hline \end{tabular} ICC = intraclass correlation coefficient, reported as 95% CI [lower limit; upper limit]; SEM = standard error of measurement; CV = coefficient of variation; Bland–Altman analysis reported as mean bias [lower LoA; upper LoA], outliers; LoA = limits of agreement.  
\end{table} 
Table 2: _Cont._areas where photogrammetric measurement remains problematic and offering insights into potential sources of error. This study has several limitations. Although the sample size of 53 participants was adequate for statistical power (\(\approx\)0.9 from post hoc power analysis), it limits generalisability, especially since stringent eligibility criteria were used, and individuals with obesity or older adults were not included. Convenience sampling may have overrepresented certain body shapes. Furthermore, the findings are specific to the particular photogrammetric technology and the scanning conditions used (including resolution, lighting, and subject positioning), which may differ from other systems such as high-precision 3D laser scanners. Importantly, the detection of proportional bias in some anatomical regions indicates measurement error that may be body size dependent. Moreover, we did not assess precision in terms of repeatability and reproducibility through replicate observations, nor did we compare our system against other photogrammetric technologies, which limits the clinical applicability of the results. The findings have important practical implications. Digital anthropometric technologies offer promising alternatives to manual anthropometry for applications in nutritional assessment, ergonomics, and musculoskeletal health monitoring [17]. The reliability demonstrated in limb measurements suggests these systems can improve efficiency in settings where manual measurements are currently standard. However, before clinical or high-stakes applications can adopt this technology broadly, the limitations in trunk measurements must be addressed. Possible improvements include refining photogrammetric reconstruction of hidden regions like the armpits (for example, using images with arms raised to a 90-degree angle), better standardisation of breathing protocols during scans, and improving 3D reconstruction algorithms to account for individual body morphology [4,20]. These developments could extend the use of photogrammetry to healthcare, sports science, and apparel industries, where fast, reliable, and non-contact body measurement is increasingly valuable. Additionally, this research informs ongoing debates about the integration of automated measurement tools into clinical and research workflows, highlighting both their potential and their current limitations.