Prespecified held-out in-silico validation of a failure-informed plantar-contact algorithm under sensor and transport perturbations

Abstract

Wearable gait-event algorithms may fail when plantar-pressure signals are affected by noise, drift, channel mismatch, data loss, or non-walking load cycles. We compared the NUWAC gait-event and plantar-contact classifier R4 with frozen R3 in a prespecified held-out in-silico validation against synthetic latent truth. Both algorithms were frozen before unseen fixtures were generated once, locked, and processed through the exact RAW20 path. We evaluated 31 positive conditions (310,000 contacts per algorithm), nine negative controls (54,000 synthetic seconds per algorithm), and temporal resolution. R4 met all six superiority criteria and all non-inferiority guardrails. R4-minus-R3 improvements were 23.47 percentage points for classification at 5% noise, 14.00 points for detection F1 at 10% noise, 42.05 points for classification at 10% noise, 52.00 points for recall at 0.3%/s drift, 8.21 points for recall at offset SD 25 counts, and 24.01 points for classification under combined-moderate stress. R4 produced no false steps across the negative controls but made later decisions under noise and combined stress. Residual boundaries included 20% noise, large offset mismatch, noHeel contacts under severe perturbation, and a small recall loss under 20% gain variation. These findings establish algorithmic behavior under specified computational conditions, not physical-device or clinical validity. Preprint status: This manuscript has been submitted to the Journal of Medical Engineering & Technology and has not yet undergone peer review.