Accurately detecting multiple simultaneous touches is crucial for various applications using piezoresistance sensor arrays. However, calibrating them is difficult due to their nonlinearity and hysteresis. While data-driven deep learning approaches could model complex sensor patterns, the required amount of labeled data increases exponentially as the number of contact points or sensor subelements increases. In this letter, we propose a novel supervised learning framework, Local Message Passing Network, that only needs single touch data to calibrate multiple contact points into a high resolution pressure map. The individual sub-local networks eliminate domain shift problems, while a message passing mechanism enables them to correctly learn correlations between neighboring sensor subelements. The performances of the proposed model were tested on labeled single- and double-pressure data and compared with previous deep learning calibration methods. Experimental results show that our framework can expand prior knowledge of single touch data to calibrate multi-touch sensor inputs into high resolution pressure maps.

中文翻译：

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单到多：数据驱动的压阻传感器阵列高分辨率校准方法


准确检测多个同时触摸对于使用压阻传感器阵列的各种应用至关重要。然而，由于它们的非线性和滞后性，校准它们很困难。虽然数据驱动的深度学习方法可以对复杂的传感器模式进行建模，但随着接触点或传感器子元件数量的增加，所需的标记数据量呈指数级增长。在这封信中，我们提出了一种新颖的监督学习框架，即本地消息传递网络，它只需要单次触摸数据即可将多个接触点校准为高分辨率压力图。各个子本地网络消除了域转移问题，而消息传递机制使它们能够正确学习相邻传感器子元件之间的相关性。所提出模型的性能在标记的单压力和双压力数据上进行了测试，并与以前的深度学习校准方法进行了比较。实验结果表明，我们的框架可以扩展单点触摸数据的先验知识，以将多点触摸传感器输入校准为高分辨率压力图。