Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift
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David Regev, Eran Beeri Bamani · arXiv: 2610.39704v1 · Summary page
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Cleaner movement data can change the movement being measured
Video-based gait analysis often discards poses that software tracks with low confidence. But difficult-to-track movement may itself carry information. Researchers compared confidence-based filtering with random selection using the same observation budget in a Parkinson-related cohort and an NF2-related replication cohort. In the Parkinson-related data, filtering shifted the movement summary by +0.05 SD, while held-out prediction error for a clinical balance test stayed essentially unchanged. Preserving the proportions of different movement groups reduced the shift.
The study combined cohort analysis with simulations separating measurement error from selection effects. Filtering helped when confidence identified noisy measurements, but could bias the endpoint when confidence tracked movement more strongly than error. The NF2 cohort showed no shift, and its predictor did not outperform a constant baseline. The broader warning is about measurement itself: a stable downstream prediction score cannot establish that a cleaning procedure has preserved the phenomenon under study.
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