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 surface of clothing, 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 the same as a conventional
shirt while providing exciting pose-tracking capabilities. To validate this
approach, we implemented a proof-of-concept untethered shirt. With eight
capacitive sensing seams, our customized deep-learning pipeline accurately
estimates the upper-body 3D joint positions relative to the pelvis. With a
12-participant user study, we demonstrated promising cross-user and
cross-session tracking performance. SeamPose represents a step towards
unobtrusive integration of smart clothing for everyday pose estimation.

我们在连衣裙制造过程中重新利用接缝线作为连续上半身姿势估计的电容传感器，比起将电极直接放置在衣物表面的以往所有织物运动捕捉服装，我们的解决方案通过在接缝线上缝制绝缘导电线来利用衬衫内部的现有接缝线。该方案利用接缝线的独特不可见性和位置，使得感知衬衫看起来和穿着方式与传统衬衫相同，并提供令人兴奋的姿势跟踪能力。通过实施一个无线连衣裙的概念验证，我们的定制深度学习流水线准确地估计相对于骨盆的上半身三维关节位置。通过一项包括 12 名参与者的用户研究，我们展示了有希望的跨用户和跨会话跟踪性能。SeamPose 代表了实现智能服装与日常姿势估计无缝融合的一大步骤。

SeamPose: 利用衬衫上的接缝作为电容传感器进行上身姿态跟踪

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

Textile sensors transform our everyday clothing into a means to track
movement and bio-signals in a completely unobtrusive way. One major hindrance
to the adoption of "smart" clothing is the difficulty encountered with
connections and space when scaling up the number of sensors. There is a lack of
research addressing a key limitation in wearable electronics: connections
between rigid and textile elements are often unreliable and they require
interfacing sensors in a way incompatible with textile mass production methods.
We introduce a prototype garment, compact readout circuit, and algorithm to
measure localized strain along multiple regions of a fibre. We employ a helical
auxetic yarn sensor with tunable sensitivity along its length to selectively
respond to strain signals. We demonstrate distributed sensing in clothing,
monitoring arm joint angles from a single continuous fibre. Compared to optical
motion capture, we achieve around 5{\deg} error in reconstructing shoulder,
elbow, and wrist joint angles.

利用纺织传感器跟踪运动和生物信号，解决可穿戴电子设备中刚性与纺织元件连接不可靠且与纺织大规模生产方式不兼容的问题，介绍了原型服装、紧凑读出电路和算法以测量纤维多个区域的局部应变，并使用可调敏感性的螺旋辅助纤维传感器对应变信号进行选择性响应，演示了在服装中的分布式传感和从单个连续纤维监测手臂关节角度，其对肩、肘和腕关节角度的重建误差率约为 5 度。