[
    {
        "id": "osp-24444",
        "type": "article-journal",
        "title": "Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose",
        "author": [
            {
                "family": "Guo",
                "given": "Zhilin"
            },
            {
                "family": "Zhang",
                "given": "Boqiao"
            },
            {
                "family": "Urbán",
                "given": "Oszkár"
            },
            {
                "family": "Bengtson",
                "given": "Josef"
            },
            {
                "family": "Aktas",
                "given": "Hakan"
            },
            {
                "family": "Li",
                "given": "Wenzhao"
            },
            {
                "family": "Hong",
                "given": "Siyu"
            },
            {
                "family": "Fogarty",
                "given": "Kyle"
            },
            {
                "family": "Zhou",
                "given": "Chenliang"
            },
            {
                "family": "Senguel",
                "given": "Ali"
            },
            {
                "family": "Oztireli",
                "given": "Cengiz"
            }
        ],
        "URL": "https://omanscience.com/en/articles/reliability-gated-fusion-of-consumer-head-and-foot-imus-for-lower-body-3d-pose",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]