Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

翻译：针织传感器常因固有特性（如偏移、松弛和漂移）而产生不一致性。这些特性共同导致可靠地将传感器数据映射到物理驱动行为变得困难。本文通过结合简单的预处理，并运用最小化人工神经网络（ANN）进行处理，展示了一种应对该问题的方法。我们对重采样后的传感器信号应用多个指数平滑滤波器，生成保留不同层级历史传感器数据的特征，这些特征组合后能表征前期传感器驱动的充分状态。通过训练一个包含8个神经元的三层人工神经网络，我们显著改善了传感器读数与驱动力之间的映射效果。研究结果还表明，该技术可迁移至材质和结构存在合理差异的传感器，并进一步适用于应变等相关物理特征。