Knitted Sensors: Location Identification

Recent work has shown the feasibility of producing knitted capacitive touch sensors through digital fabrication with little human intervention in the textile production process. Such sensors can be designed and manufactured at scale and require only two connection points, regardless of the touch sensor form factor and size of the fabric, opening many possibilities for new designs and applications in textile sensors. To bring this technology closer to real-world use, we go beyond previous work on coarse touch discrimination to enable fine, accurate touch localization on a knitted sensor, using a recognition model able to capture the temporal behavior of the sensor. Moreover, signal acquisition and processing are performed in real-time, using swept frequency Bode analysis to quantify distortion from induced capacitance. After training our network model, we conducted a study with new users, and achieved a subject-independent accuracy of 66%in identifying the touch location on the36-button sensor, while chance accuracy is approximately 3%. Additionally, we conducted a study demonstrating the viability of taking this solution closer to real-world scenarios by testing the sensor’s resistance to potential deformation from everyday conditions. We also introduce several other knitted designs and related application prototypes to explore potential uses of the technology.



Denisa Qori McDonald, Richard James Vallet, Erin Solovey, Genevieve Dion, and Ali Shokoufandeh. Knitted Sensors: Designs and Novel Approaches for Real-Time, Real-World Sensing. Proc. of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 2020. (to be presented at Ubicomp 2021).