# Smartphone-Based Automated Photogrammetry for Reconstruction of Residual Limb Models in Prosthetic Design

## TL;DR

A smartphone-based photogrammetry method can create accurate 3D models of residual limbs for prosthetic design, offering a low-cost and scalable solution.

## Contribution

A fully automated photogrammetry pipeline using smartphones achieves sub-millimeter accuracy for prosthetic modeling.

## Key findings

- Smartphone video-based photogrammetry achieves sub-millimeter surface accuracy and <±1% volume/perimeter error compared to CT-derived ground truth.
- The pipeline meets clinical accuracy and repeatability thresholds for 95% of scans across diverse limb geometries.
- Acquisition time is under five minutes with reconstruction completed in about 1 h and 30 min.

## Abstract

What are the main findings?
A fully automated photogrammetry pipeline using only a smartphone or consumer camera achieves sub-millimeter surface accuracy and <±1% volume/perimeter error compared to CT-derived ground truth.Smartphone video-based photogrammetry performs robustly across diverse limb geometries, meeting all predefined clinical accuracy and repeatability thresholds for 95% of the scans.

A fully automated photogrammetry pipeline using only a smartphone or consumer camera achieves sub-millimeter surface accuracy and <±1% volume/perimeter error compared to CT-derived ground truth.

Smartphone video-based photogrammetry performs robustly across diverse limb geometries, meeting all predefined clinical accuracy and repeatability thresholds for 95% of the scans.

What are the implications of the main findings?
The pipeline offers a low-cost, scalable, and operator-independent alternative for prosthetic residual-limb modeling, suitable for routine clinical use and longitudinal monitoring.Its accessibility and automation enable deployment in resource-limited or remote care environments, supporting more equitable and data-driven prosthetic socket design.

The pipeline offers a low-cost, scalable, and operator-independent alternative for prosthetic residual-limb modeling, suitable for routine clinical use and longitudinal monitoring.

Its accessibility and automation enable deployment in resource-limited or remote care environments, supporting more equitable and data-driven prosthetic socket design.

Accurate modeling of residual limb geometry is essential for prosthetic socket design, yet current scanning techniques can be costly, operator-dependent, or impractical for repeated clinical use. This study presents a fully automated, low-cost photogrammetry workflow capable of generating metrically accurate 3D models of lower-limb residual limbs using video and still images acquired with a standard smartphone or a full-frame digital camera. The pipeline integrates adaptive frame selection, deep learning-based background removal, robust metric scaling via ArUco markers, and open-source Structure-from-Motion and Multi-View Stereo reconstruction, requiring no manual post-processing or proprietary software. Accuracy and repeatability were evaluated using four 3D-printed limb phantoms and high-resolution CT-derived meshes as ground truth. Smartphone video and full-frame camera acquisitions achieved sub-millimeter surface accuracy, volume and perimeter errors within ±1%, and high inter-session repeatability, all within clinically accepted thresholds for prosthetic socket fabrication. In contrast, smartphone still-photo reconstructions showed larger deviations and reduced stability. Acquisition time was under five minutes, and complete reconstruction required approximately 1 h and 30 min. These results demonstrate that smartphone video-based photogrammetry provides a practical, scalable, and clinically viable alternative for residual limb modeling, particularly in resource-constrained or remote care settings.