MARKERLESS MOTION CAPTURE FOR CLINICAL MOVEMENT ANALYSIS AND THE INFLUENCE OF CLOTHING ON GAIT METRICS
Bibliographic record
Abstract
For clinical movement analysis, optical marker-based motion capture is the gold standard. With the advancement of AI-driven computer vision, markerless motion capture (MMC) has emerged. Validity against the marker-based standard has only been examined for lightly-dressed subjects as required for marker placement. This pilot study investigates how different clothing affects the measurement of typical gait metrics. Gait tests at self-selected speed (4 km/h) were performed on a treadmill (Motek Grail), captured by 9 cameras (Qualisys Miqus, 720p, f=100Hz) and analyzed by a leading MMC application (Theia, Canada). A healthy subject (female, h=164cm, m=54kg) donned clothes between trials starting from lightly dressed (LD: bicycle tight, short-sleeved shirt), adding a short skirt (SS: hip occlusion) or a midi-skirt (MS: partial knee occlusion) or street wear (SW: jeans covering ankle, long-sleeved blouse), the lattern combined with a short jacket (SWJ) or a long coat (SWC). Gait parameters (mean±SD, t=10s) calculated (left leg, mid-stance) were ankle pronation (AP-M), knee flexion (KF-M), pelvic obliquity (PO-M) and trunk lateral lean (TL-M) representing clinically common metrics, different joints and anatomic planes. Four repetitions of the base style (LD) were compared to states of increased garment coverage using the t-test (Bonferroni correction). For most gait metrics, differences between the light dress (LD) and various clothing styles were absent (p>0.0175), small (< 2SD) or below the minimal clinically important differences (MCID). For instance, KF-M was for LD=10.5°±1.7 versus MD=12.0°±0.5 (p=0.07) despite partial knee cover. AP-M measured for LD=5.2°±0.6 versus SW=4.1°±0.7 (p<0.01) despite ankle cover-up. The difference for KF-M between LD=10.5°±1.7 versus SWL=6.0°±0.9, SW and SWJ (7.6°±1.5, p<0.01) indicates more intra-subject gait variability than clothing effect. This study suggests that typical clothings styles only have a small clinically possibly negligible effect on common gait parameters measured with MMC. Thus, patients may not need to change clothes or be instructed to wear specific garments. In addition to avoiding marker placement, this further increases speed, ease and economy of clinical gait analysis with MMC facilitating high volume or routine application.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".