Deformation Capture via Soft and Stretchable Sensor Arrays
Deformation Capture via Soft and Stretchable Sensor Arrays
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DOI：
10.1145/3311972
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发表时间：
2019-04-01
影响因子：
6.2
通讯作者：
Sorkine-Hornung, Olga
中科院分区：
文献类型：
--
作者：
Glauser, Oliver;Panozzo, Daniele;Sorkine-Hornung, Olga
我们提出了一种硬件和软件管道来制造柔性可穿戴传感器，并使用它们来捕捉无视线的变形。我们的第一个贡献是低成本的制造管道，将具有复杂几何形状的多个对齐导电层嵌入有机硅化合物中。来自不同层的重叠导电区域形成局部电容器，测量密集区域的变化。与现有的制造方法相反，所提出的技术只需要在现代实验室中随时可用的硬件。虽然面积测量本身不足以重建表面的完整3D变形，但当与数据驱动的先验配对时，它们就足够了。一种基于弹性表面几何变形的新型半自动跟踪算法，使我们能够使用光学运动捕捉系统捕获地面真实数据，即使在严重遮挡或部分不可观察的标记下也是如此。结果数据集用于训练基于深度神经网络的回归器，直接将区域读数映射到表面顶点的全局位置。我们在一系列的控制实验中证明了所提出的硬件和软件的灵活性和准确性，并设计了一个可穿戴手腕、肘部和二头肌传感器的原型，它不需要视线，可以穿在普通衣服下面。
We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line-of-sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows us to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments and design a prototype of wearable wrist, elbow, and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.