From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915748683710464 |
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| author | Liu, Mengxi Ray, Lala Shakti Swarup Bian, Sizhen Watanabe, Ko Bhatt, Ankur Sorysz, Joanna Torah, Russel Zhou, Bo Lukowicz, Paul |
| author_facet | Liu, Mengxi Ray, Lala Shakti Swarup Bian, Sizhen Watanabe, Ko Bhatt, Ankur Sorysz, Joanna Torah, Russel Zhou, Bo Lukowicz, Paul |
| contents | We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklace-style form factor. NeckSense captures dynamic changes in tissue impedance around the neck, which are modulated by head rotations and subtle muscle activations. To robustly estimate head pose, we propose a deep learning framework that integrates anatomical priors, including joint constraints and natural head rotation ranges, into the loss function design. We validate NeckSense on 7 participants using the current SOTA pose estimation model as ground truth. Our system achieves a mean per-vertex error of 25.9 mm across various head movements with a leave-one-person-out cross-validation method, demonstrating that a compact, line-of-sight-free bio-impedance wearable can deliver head-tracking performance comparable to SOTA vision-based methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation Liu, Mengxi Ray, Lala Shakti Swarup Bian, Sizhen Watanabe, Ko Bhatt, Ankur Sorysz, Joanna Torah, Russel Zhou, Bo Lukowicz, Paul Computer Vision and Pattern Recognition Signal Processing We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklace-style form factor. NeckSense captures dynamic changes in tissue impedance around the neck, which are modulated by head rotations and subtle muscle activations. To robustly estimate head pose, we propose a deep learning framework that integrates anatomical priors, including joint constraints and natural head rotation ranges, into the loss function design. We validate NeckSense on 7 participants using the current SOTA pose estimation model as ground truth. Our system achieves a mean per-vertex error of 25.9 mm across various head movements with a leave-one-person-out cross-validation method, demonstrating that a compact, line-of-sight-free bio-impedance wearable can deliver head-tracking performance comparable to SOTA vision-based methods. |
| title | From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation |
| topic | Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2507.12884 |