A Differential Capacitive Sensing System for Knitted Textiles

Abstract

Textiles, in combination with advances in materials, intelligent sensing and signal processing, offer exciting new possibilities for fabric-based human interfaces and flexible, touch-sensitive devices. This thesis introduces a novel touch detection method and applies signal processing techniques towards measuring distributed capacitive touch across knitted fabric circuits. This work introduces differential capacitive sensing--a technique to measure capacitive touch along a continuous linear conductor using paired input and output voltage waveforms. A framework for constructing compatible knitted circuits from conductive yarns is introduced that enables the creation of extensible fabric touch interfaces using digital weft knitting. Touch localization, through differential capacitive sensing, is improved by applying Bode analysis to measure frequency-specific signal attenuation invariant to distortion from electromagnetic interference. A real-time sensing controller is constructed to generate, acquire and process voltage waveforms measured from the fabric circuit. The processed data are recorded as a time series of paired gain measurements with respect to input frequency. The contributions of this work make possible the development of Mixed-Source Description, a method to extract scale-space features from time-frequency measurements. A scale-space representation of temporal gestures, such as press-and-release, is constructed and compared using a novel distance metric, the Euclidean Levenshtein Distance, developed to quantify the similarity between temporal touch data. This work provides the foundation for quantifying high-level user input using textile-based sensors and a robust capacitive touch sensing system.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 202