  Developments in touch-sensitive textiles have enabled many novel interactive
techniques and applications. Our digitally-knitted capacitive active sensors
can be manufactured at scale with little human intervention. Their sensitive
areas are created from a single conductive yarn, and they require only few
connections to external hardware. This technique increases their robustness and
usability, while shifting the complexity of enabling interactivity from the
hardware to computational models. This work advances the capabilities of such
sensors by creating the foundation for an interactive gesture recognition
system. It uses a novel sensor design, and a neural network-based recognition
model to classify 12 relatively complex, single touch point gesture classes
with 89.8% accuracy, unfolding many possibilities for future applications. We
also demonstrate the system's applicability and robustness to real-world
conditions through its performance while being worn and the impact of washing
and drying on the sensor's resistance.
