Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose

2026-09-28

AI Summary

This study addresses the unreliability of sparse inertial pose estimation with consumer-grade IMUs caused by firmware discrepancies, wearing variations, and signal drift. To tackle these challenges, we propose a channel-level reliability gating fusion method that adaptively learns trust weights for each sensor channel via a temporal gating network. The model is optimized through synthetic data pretraining combined with an auxiliary reliability objective function. Furthermore, we construct a benchmark dataset comprising 35 recording sessions across head-mounted earphones and foot-worn smart insoles. Experimental results demonstrate that the proposed approach achieves state-of-the-art accuracy (69.4 mm) under both clean data conditions and simulated failure scenarios, effectively suppressing sensor bias and enabling dropout detection.

Abstract

Sparse inertial pose estimation promises camera-free motion capture from consumer devices, but consumer sensors are unreliable: firmware-fused orientations are biased, mounting varies between sessions, and streams drift or drop out. On a new 35-take single-subject benchmark pairing an earbud head inertial measurement unit (IMU) with two smart-insole foot IMUs (SAM-3D-Body pseudo-ground-truth labels), we show the reliability problem is channel-level: a channel ablation isolates foot acceleration as the most informative input (66.6 mm vs. 79.0 mm head-only) and the firmware-fused foot orientation as the liability that destroys the gain. We therefore let the model learn how much to trust each channel of each stream: one temporal gate per stream per channel block, trained with an auxiliary reliability objective on synthetically corrupted pretraining data. The channel-gated model is the most accurate of our learned fusion arms on clean data (69.4 mm vs. 83.7 static, 86.6 ungated) and under every simulated fault (bias in training; drift, dropout eval-only); its gates suppress the natively biased foot-orientation channels on clean real data without test-time supervision and flag dropout bursts at 0.92-0.999 AUROC. Two contrasts: dropping a channel known a priori to fail is flat across foot faults but collapses when an unanticipated stream fails (head dropout: 92.9 vs. 79.3 mm); and a fine-tuned HMD-Poser is more accurate on clean data (64.4 mm) and nominally under drift, with no significant paired difference under bias or dropout, but a larger worst-case degradation from clean (+16.1 vs. +3.5 mm, single seed). Learning to gate reliability instead of sensor count is the lever for deployable sparse inertial capture. Code is available at https://github.com/ZhilinGuo/reliability-gated-imu-fusion.

Problem

• sparse inertial pose estimation
• consumer IMU reliability
• lower-body 3D pose
• sensor fusion
• data dropout and drift

Innovation

• Reliability-Gated Fusion
• Sparse Inertial Pose Estimation
• Consumer IMUs
• Channel-Level Gating
• Robust Sensor Fusion