Knitted Sensors: Signal Representation

Fabric sensors have been introduced to enable flexible touch-based interaction. We advance the technical capabilities of a scalable and low-profile knitted capacitive touch sensing system by introducing methods to improve its touch localization accuracy. The sensor hardware design tends toward minimalism by using a single conductive yarn and two external connections located at each endpoint. Fewer connectors simplify the textile system integration, but this comes at the expense of reduced signal information output from the system. The electrical continuity of the sensing element, essential to the process of knitting, also increases the uncertainty of localizing touch. We propose using Bode analysis to measure changes in signal due to capacitive touch, as well as design a new algorithm, Mixed-Source Description (MSD), which retains the most significant aspects of the signal in terms of touch location identification. We do not classify location of touch, but focus on an invariant signal representation. To evaluate our methods, we introduce Euclidean  Levenshtein Distance (ELD), a distance metric to compute the similarity of pairs of key-presses, generalizable to computing distances of tensors of varying lengths. Our experiments show that the proposed sensing method results in high-fidelity signals. Furthermore, the sparse representation of key-presses produced by MSD significantly increases separability between different touch locations. Possible applications based on these sensors are also illustrated through prototypes and use case descriptions. 


Richard James Vallet,  Denisa Qori McDonald, Genevieve Dion, Youngmoo Kim, and Ali Shokoufandeh. Toward Accurate Sensing with Knitted Fabric: Applications and Technical Considerations. Proc. of ACM on Human-Computer Interaction. 2019. (to be presented at EICS 2020/2021). Best Paper Award, awarded to the the top 1\% of submissions.