Textiles are familiar and pervasive materials, making them natural candidates for ubiquitous sensing. Recent research efforts on textile-based sensors span a variety of applications. However, it is challenging to manufacture these deformable and stretchable sensors at scale. On the other hand, industrial textile manufacturing is an established process. Efforts such as Project Jacquard by Google and Levis turned woven fabrics into interactive multi-touch surfaces using standard looms and processes. In contrast to these touch-sensitive fabric surfaces, knitted fabrics can be manufactured into complex objects (e.g. seam-free sweaters) at scale with minimal post-processing. Moreover, they are more stretchable and breathable than woven fabrics. In this thesis, I convert these knitted fabrics into sensors using an off-the-shelf resistive yarn and industrial knitting machines to fabricate sensing textiles. These sensors are scalable, stretchable, parameterizable, and customizable. They measure electrical properties of the conductive areas to sense contact, pressure, strain, proximity, and even its own orientations. I prototyped a multi-layer matrix-style resistive pressure sensor with robust ON/OFF behaviors and applied them to be robotic skins. Though this sensor could be instrumented on humans as well, humans are in much more diverse contexts doing different activities, so the needs for sensors vary often. Thus for humans, I prototyped a gaiter-scarf inspired multi-purpose reconfigurable accessory using electrical impedance tomography for on-body location detection, gesture recognition, and passive monitoring. Fabrication techniques proposed in this work can be further generalized beyond sensing with functional fibres to augment traditional textiles with new purposes towards a future of pervasive smart textiles.

52 pages

Thesis Committee:
Mayank Goel (Co-Chair)
James McCann (Co-Chair)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

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