A Direct Comparison of Biplanar Videoradiography and Optical Motion Capture for Foot and Ankle Kinematics
Bibliographic record
Abstract
Measuring motion of the human foot presents a unique challenge due to the large number of closely packed bones with congruent articulating surfaces. Optical motion capture (OMC) and multi-segment models can be used to infer foot motion, but might be affected by soft tissue artefact (STA). Biplanar videoradiography (BVR) is a relatively new tool that allows direct, non-invasive measurement of bone motion using high-speed, dynamic x-ray images to track individual bones. It is unknown whether OMC and BVR can be used interchangeably to analyse multi-segment foot motion. Therefore, the aim of this study was to determine the agreement in kinematic measures of dynamic activities. Nine healthy participants performed three walking and three running trials while BVR was recorded with synchronous OMC. Bone position and orientation was determined through manual scientific-rotoscoping. The OMC and BVR kinematics were co-registered to the same coordinate system, and BVR tracking was used to create virtual markers for comparison to OMC during dynamic trials. Root mean square (RMS) differences in marker positions and joint angles as well as a linear fit method (LFM) was used to compare the outputs of both methods. When comparing BVR and OMC, sagittal plane angles were in good agreement (ankle: R2 = 0.947, 0.939; Medial Longitudinal Arch (MLA) Angle: R2 = 0.713, 0.703, walking and running, respectively). When examining the ankle, there was a moderate agreement between the systems in the frontal plane (R2 = 0.322, 0.452, walking and running, respectively), with a weak to moderate correlation for the transverse plane (R2 = 0.178, 0.326, walking and running, respectively). However, root mean squared error (RMSE) showed angular errors ranging from 1.06 to 8.31 across the planes (frontal: 3.57, 3.67, transverse: 4.28, 4.70, sagittal: 2.45, 2.67, walking and running, respectively). Root mean square (RMS) differences between OMC and BVR marker trajectories were task dependent with the largest differences in the shank (6.0 ± 2.01 mm) for running, and metatarsals (3.97 ± 0.81 mm) for walking. Based on the results, we suggest BVR and OMC provide comparable solutions to foot motion in the sagittal plane, however, interpretations of out-of-plane movements should done cautiously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".