Smartphone-based automated photogrammetry for residual limb reconstruction in prosthetic 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 pros-thetic 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 90 minutes. These results demonstrate that smartphone video-based photogrammetry provides a practical, scalable, and clinically viable alternative for residual limb modeling, particularly in re-source-constrained or remote care settings.残肢几何形状的精准建模对于假肢接受腔（prosthetic socket）设计至关重要，但当前的扫描技术往往成本高昂、依赖操作人员，且难以重复应用于临床场景。本研究提出了一套全自动化、低成本的摄影测量工作流，可利用标准智能手机或全画幅数码相机采集的视频与静态图像，生成下肢残肢的度量精准3D模型。该流程整合了自适应帧选择、基于深度学习的背景去除、通过ArUco标记实现的稳健度量缩放，以及开源的运动恢复结构（Structure-from-Motion）与多视图立体（Multi-View Stereo）重建环节，无需手动后处理或专有软件支持。研究采用4个3D打印肢体模体与高分辨率CT衍生网格作为真值（ground truth），对模型的精度与可重复性进行了评估。结果显示，智能手机视频与全画幅相机采集的模型表面精度可达亚毫米级，体积与周长误差控制在±1%以内，且会话间重复性优异，全部指标均符合假肢接受腔制造的临床认可阈值。与之相对，智能手机静态照片重建结果偏差更大、稳定性更差。数据采集时长不足5分钟，完整重建流程耗时约90分钟。上述结果表明，基于智能手机视频的摄影测量技术可为残肢建模提供一种实用、可扩展且临床可行的替代方案，尤其适用于资源受限或远程医疗场景。