{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:20:07Z","timestamp":1771233607294,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00b11%, 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.<\/jats:p>","DOI":"10.3390\/s26041251","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:38:39Z","timestamp":1771231119000},"page":"1251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smartphone-Based Automated Photogrammetry for Reconstruction of Residual Limb Models in Prosthetic Design"],"prefix":"10.3390","volume":"26","author":[{"given":"Lander","family":"De Waele","sequence":"first","affiliation":[{"name":"Movement Control and Neuroplasticity Research Group, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7569-7223","authenticated-orcid":false,"given":"Jolien","family":"Gooijers","sequence":"additional","affiliation":[{"name":"Movement Control and Neuroplasticity Research Group, KU Leuven, 3001 Leuven, Belgium"},{"name":"Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6485-5559","authenticated-orcid":false,"given":"Dante","family":"Mantini","sequence":"additional","affiliation":[{"name":"Movement Control and Neuroplasticity Research Group, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"ref_1","first-page":"100059","article-title":"Perceived Effect of Socket Fit on Major Lower Limb Prosthetic Rehabilitation: A Clinician and Amputee Perspective","volume":"2","author":"Turner","year":"2020","journal-title":"Arch. 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