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。

智能服装分布式纤维传感

Distributed Sensing Along Fibres for Smart Clothing

Tactile signals collected by wearable electronics are essential in modeling
and understanding human behavior. One of the main applications of tactile
signals is action classification, especially in healthcare and robotics.
However, existing tactile classification methods fail to capture the spatial
and temporal features of tactile signals simultaneously, which results in
sub-optimal performances. In this paper, we design Spatio-Temporal Aware
tactility Transformer (STAT) to utilize continuous tactile signals for action
classification. We propose spatial and temporal embeddings along with a new
temporal pretraining task in our model, which aims to enhance the transformer
in modeling the spatio-temporal features of tactile signals. Specially, the
designed temporal pretraining task is to differentiate the time order of
tubelet inputs to model the temporal properties explicitly. Experimental
results on a public action classification dataset demonstrate that our model
outperforms state-of-the-art methods in all metrics.

利用连续触觉信号进行动作分类的一项主要应用是医疗保健和机器人技术，然而，现有的触觉分类方法未能同时捕捉触觉信号的空间和时间特征，从而导致性能不佳。本文设计了一种名为 Spatio-Temporal Aware tactility Transformer (STAT) 的模型，利用连续触觉信号进行动作分类，通过在模型中引入空间和时间嵌入以及新的时间预训练任务，旨在增强变压器对触觉信号的空间和时间特征建模能力。实验结果表明，在公共动作分类数据集上，我们的模型在所有评估指标上优于现有的方法。