Seams are areas of overlapping fabric formed by stitching two or more pieces of fabric together in the cut-and-sew apparel manufacturing process. In SeamPose, we repurposed seams as capacitive sensors in a shirt for continuous upper-body pose estimation. Compared to previous all-textile motion-capturing garments that place the electrodes on the clothing surface, our solution leverages existing seams inside of a shirt by machine-sewing insulated conductive threads over the seams. The unique invisibilities and placements of the seams afford the sensing shirt to look and wear similarly as a conventional shirt while providing exciting pose-tracking capabilities. To validate this approach, we implemented a proof-of-concept untethered shirt with 8 capacitive sensing seams. With a 12-participant user study, our customized deep-learning pipeline accurately estimates the relative (to the pelvis) upper-body 3D joint positions with a mean per joint position error (MPJPE) of 6.0 cm. SeamPose represents a step towards unobtrusive integration of smart clothing for everyday pose estimation.
@article{arxiv.2406.11645,
title = {SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking},
author = {Tianhong Catherine Yu and Manru Mary Zhang and Peter He and Chi-Jung Lee and Cassidy Cheesman and Saif Mahmud and Ruidong Zhang and François Guimbretière and Cheng Zhang},
journal= {arXiv preprint arXiv:2406.11645},
year = {2024}
}