SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking

TL;DR

SeamPose uses existing shirt seams as hidden capacitive sensors for accurate, unobtrusive upper-body pose tracking, demonstrating promising results with a deep learning approach in a user study.

Contribution

This work introduces a novel method of repurposing shirt seams as capacitive sensors for pose estimation, combining invisibility with effective tracking capabilities.

Findings

Achieved a mean per joint position error of 6.0 cm in user study.

Demonstrated feasibility of seamless, unobtrusive smart clothing for pose tracking.

Validated the approach with an untethered prototype and deep learning pipeline.

Abstract

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.