Capacitivo
Te-Yen Wu, Lu Tan, Yuji Zhang, Teddy Seyed, Xing-Dong Yang
Abstract
We present Capacitivo, a contact-based object recognition technique developed for interactive fabrics, using capacitive sensing. Unlike prior work that has focused on metallic objects, our technique recognizes non-metallic objects such as food, different types of fruits, liquids, and other types of objects that are often found around a home or in a workplace. To demonstrate our technique, we created a prototype composed of a 12 x 12 grid of electrodes, made from conductive fabric attached to a textile substrate. We designed the size and separation between the electrodes to maximize the sensing area and sensitivity. We then used a 10-person study to evaluate the performance of our sensing technique using 20 different objects, which yielded a 94.5% accuracy rate. We conclude this work by presenting several different application scenarios to demonstrate unique interactions that are enabled by our technique on fabrics.