MARKERLESS MOTION CAPTURE FOR ASSESSING FRONTAL PLANE LOWER LIMB ALIGNMENT DURING STATIC AND DYNAMIC TASKS
Bibliographic record
Abstract
Lower limb alignment and specifically the hip-knee-ankle-angle (HKAA), is increasingly being recognized as an important factor in operative planning for total knee arthroplasty (TKA) for patients with knee osteoarthritis (OA). Current clinical practice is to measure alignment from weight bearing radiographs. This static measurement may differ from alignment during the weightbearing stance phase of gait. Markerless motion capture is a novel biomechanical assessment tool that uses off-the-shelf video cameras to capture whole-body movements. The objective of our study was to evaluate HKAA during static and dynamic tasks for patients with predominantly medial or lateral knee OA. Patients diagnosed with knee OA were recruited from advanced care physiotherapists at an outpatient hospital. Markerless motion capture was used to measure whole body movements during a quiet standing task and during gait using eight synchronized Sony RX0 II video cameras at 60 Hz (Sony, Minato, Japan). During quiet stand, subjects stood upright with their feet facing forward and shoulder-width apart for thirty seconds. Gait was assessed during overground walking at a self-selected speed for one minute. Video data was processed with Theia3D (v. 2023.01.0.361 p7, Theia Markerless Inc. Kingston, ON). Visual3D (C-Motion, Germantown, MD) was used to provide gait event detection and estimate hip, knee, and ankle joint locations in three-dimensions at each frame. HKAA was calculated from the three-dimensional joint locations in the frontal plane. For the static quiet standing task, HKAA was averaged over the first five seconds of the standing task. During gait, dynamic HKAA was calculated as the peak angle prior to terminal stance. Correlation between static and dynamic HKAA were calculated and compared using the Bland and Altman approach. Classification of medial or lateral knee OA was done by an orthopaedic surgeon via radiographic evaluation. Dynamic HKAA was compared between medial and lateral knee OA patients (t-test). Static and dynamic HKAA were assessed for 160 knees in 102 patients (34 male, 68 female, mean age 67 years (SD 9)). The static HKAA from the quiet standing task (mean = 182.7 degrees, SD = 4.2) and the peak dynamic HKAA from the gait task (mean = 184.7 degrees SD = 4.6) were highly correlated (Pearson's r = 0.88, p < 0.001). Dynamic HKAA was on average two degrees more varus . Dynamic HKAA was significantly more varus for patients with medial OA (mean 185.9 degrees) than those with lateral OA (mean 176.7 degrees, p < 0.001, t-test). Dynamic peak HKAA measured with markerless motion capture was found to be strongly correlated with static HKAA. Markerless motion capture provides an alternative, low burden, radiation free assessment tool with high potential for clinical integration. The ability to assess dynamic tasks and dynamic parameters, such as HKAA throughout gait, means that markerless motion capture has the potential to provide enhanced assessments pre- and post-TKA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".