Related Experiment Video
Updated: Jan 18, 2026
Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
Published on: March 14, 2017
Smartphone photogrammetry for prosthetics and orthotics: accuracy and reliability across upper-limb, lower-limb, and
Arthur Ellis1,2, Vishal Pendse1,3, Calvin C Ngan1
1Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, M4G 1R8, Canada.
Background:
Conventional shape capture in prosthetics and orthotics (P&O) relies on plaster casting of a positive mold, which is then hand-rectified to the desired shape. While effective under expert practice, this workflow is labor-intensive, equipment-dependent, and difficult to archive or share. Digital approaches using structured-light scanners address some of these limitations but remain costly and require dedicated training. This study evaluated whether smartphone photogrammetry can accurately and reliably capture prosthetic and orthotic cast geometries, and assessed its usability in comparison to a clinical structured-light scanner for integration into clinical workflows.
Results:
A clinical-grade structured-light scanner (EinScan H2) served as the reference and demonstrated small volumetric and dimensional errors, at 0.21 ± 0.15% and 0.35 ± 0.18 mm, with intraclass correlation coefficients (ICCs) greater than 0.9999. Relative to this reference, across 12 cast models (upper limb, lower limb, and ankle-foot orthosis), smartphone photogrammetry achieved a volumetric error of 0.89 ± 0.68% and a dimensional error of 0.89 ± 0.51 mm; the mean surface point-to-point distance was 0.24 ± 0.19 mm. Reliability across operators was near-perfect (ICCs ≥ 0.9997). Usability data showed approximately 62 photographs and 88 s per capture for photogrammetry (about 12 min cloud processing) versus 34 s capture (about 85 s desktop processing) for the reference scanner. Photogrammetry scored higher on the System Usability Scale (79 versus 58).
Conclusions:
On casts, smartphone photogrammetry produced accurate and reliable meshes with favorable perceived usability and minimal hardware demands. These findings support its integration into digital workflows in P&O, particularly for scanning rectified positive casts and other stable geometries. Further multi-site evaluations on live limbs should determine acceptable capture-time thresholds and effective stabilization strategies to ensure clinical feasibility in routine practice.
