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.

翻译：触敏纺织品的发展催生了许多新颖的交互技术与应用。我们研发的数字针织电容式有源传感器能在极少人工干预下实现规模化生产，其敏感区域由单根导电纱线构成，仅需少量外部硬件连接。这种设计在将交互功能的复杂性从硬件转移至计算模型的同时，增强了传感器的鲁棒性与可用性。本研究通过构建交互式手势识别系统的基础，拓展了此类传感器的能力边界。我们采用新型传感器设计及基于神经网络的识别模型，对12类相对复杂的单触点手势实现了89.8%的分类准确率，为未来应用开辟了广阔可能。通过佩戴状态下的性能表现及洗涤干燥对传感器电阻影响的测试，我们进一步验证了该系统在真实环境中的适用性与鲁棒性。