IDLab-AIRO, Ghent University, Interuniversity Microelectronics Centre (IMEC), Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium.
Centre for Microsystems Technology (CMST), Ghent University, Interuniversity Microelectronics Centre (IMEC), Technologiepark-Zwijnaarde 126, 9052 Zwijnaarde, Belgium.
Sensors (Basel). 2021 Dec 29;22(1):222. doi: 10.3390/s22010222.
Smart textiles have found numerous applications ranging from health monitoring to smart homes. Their main allure is their flexibility, which allows for seamless integration of sensing in everyday objects like clothing. The application domain also includes robotics; smart textiles have been used to improve human-robot interaction, to solve the problem of state estimation of soft robots, and for state estimation to enable learning of robotic manipulation of textiles. The latter application provides an alternative to computationally expensive vision-based pipelines and we believe it is the key to accelerate robotic learning of textile manipulation. Current smart textiles, however, maintain wired connections to external units, which impedes robotic manipulation, and lack modularity to facilitate state estimation of large cloths. In this work, we propose an open-source, fully wireless, highly flexible, light, and modular version of a piezoresistive smart textile. Its output stability was experimentally quantified and determined to be sufficient for classification tasks. Its functionality as a state sensor for larger cloths was also verified in a classification task where two of the smart textiles were sewn onto a piece of clothing of which three states are defined. The modular smart textile system was able to recognize these states with average per-class F1-scores ranging from 85.7 to 94.6% with a basic linear classifier.
智能纺织品在健康监测到智能家居等领域有着广泛的应用。它们的主要吸引力在于其灵活性，这使得在衣物等日常物品中无缝集成传感成为可能。应用领域还包括机器人技术；智能纺织品已被用于改善人机交互、解决软机器人的状态估计问题，以及进行状态估计以实现机器人对纺织品的操纵学习。后者的应用为计算成本高昂的基于视觉的管道提供了替代方案，我们相信这是加速机器人学习纺织品操作的关键。然而，当前的智能纺织品仍然通过有线连接与外部单元保持连接，这阻碍了机器人的操作，并且缺乏模块化，难以对大型衣物进行状态估计。在这项工作中，我们提出了一种开源、全无线、高度灵活、轻便且模块化的压阻式智能纺织品。我们通过实验对其输出稳定性进行了量化，确定其足以用于分类任务。我们还通过将两个智能纺织品缝制在一件衣物上的分类任务验证了其作为大型衣物状态传感器的功能，该衣物有三种状态。模块化智能纺织品系统能够使用基本线性分类器以平均每个类别的 F1 分数 85.7%至 94.6%识别这些状态。
