From Hidden to Handy: Making Bias Explicit and Actionable in mmWave Pose Estimation

Practical mmWave sensing systems operate with sparse returns, specular reflections, and occlusions, pushing pose estimators to lean on statistical priors rather than sensor evidence. This systems-level reality yields biased joint predictions that hurt downstream tasks (e.g., gesture/activity recognition). We present mmJoints, which augments a pre-trained, black-box mmWave 3D pose estimator with per-joint descriptors that characterize the degree of local radar support and the reliability of each predicted joint location. Instead of hiding bias, mmJoints makes it explicit and usable—improving interpretability of pose and accuracy of downstream models. Evaluated on over 115,000 frames across 13 pose-estimation settings, mmJoints consistently estimates informative $\xi$ and $\kappa$ descriptors across diverse models and sensing conditions, improves joint accuracy by up to 12.5%, and boosts activity recognition accuracy by up to 16%.