# Cross-Location IMU Translation

Moving a wearable inertial sensor to another body location shifts the signal distribution, and measurements at the desired target location are often unavailable during training or deployment. DiffusionRef-IMU first has a deterministic Direct network estimate the target signal, then lets a diffusion model correct only the residual between that estimate and the paired target. Across five subject-disjoint folds with activity–location compositions held out, 16-step DDIM reached a mean nRMSE of 0.087538, below 0.090262 for Direct, 0.088847 for a capacity-matched deterministic residual model and 0.088417 for eight-step CompFlow, and was better in all five folds.

Moving a wearable inertial sensor to another body location shifts the signal distribution, and measurements at the desired target location are often unavailable during training or deployment. DiffusionRef-IMU first has a deterministic Direct network estimate the target signal, then lets a diffusion model correct only the residual between that estimate and the paired target. Across five subject-disjoint folds with activity–location compositions held out, 16-step DDIM reached a mean nRMSE of 0.087538, below 0.090262 for Direct, 0.088847 for a capacity-matched deterministic residual model and 0.088417 for eight-step CompFlow, and was better in all five folds.