We demonstrate time-, space-, and weight-based calibration techniques for pressure monitoring in the low-cost plastic optical fiber (POF) sensor carpet. It is found that the POF-based pressure sensing platform has several limitations, such as output voltage variations and difference in the sensor response for horizontal and vertical fibers due to convex and concave shapes of bend. To surpass the these limitations, we have developed time-based calibration using time windowing technique to reduce the standard deviation of photodiode output voltage variations by 98%. We have also described space and weight calibration techniques based on the convex and concave shapes of fiber bend. These techniques help in improving the Landweber image reconstruction algorithm to obtain significant clarity and improvement in object pressure images. We also report an artificial intelligence-based algorithm to determine the accuracy of positioning of the load. Experimental results demonstrate that this algorithm gives a mean square error of 0.875 cm in position detection on the carpet. We discuss the potential for a compact and cost-effective pressure sensor carpet, which integrates with the living environment and the outside world.

中文翻译：

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时空重量校准塑料光纤压力传感地毯

我们展示了基于时间、空间和重量的校准技术，用于低成本塑料光纤 (POF) 传感器毯中的压力监测。发现基于 POF 的压力传感平台有几个局限性，例如由于弯曲的凸凹形状导致的水平和垂直纤维的输出电压变化和传感器响应的差异。为了克服这些限制，我们开发了使用时间窗口技术的基于时间的校准，以将光电二极管输出电压变化的标准偏差降低 98%。我们还描述了基于光纤弯曲的凸面和凹面形状的空间和权重校准技术。这些技术有助于改进 Landweber 图像重建算法，以获得物体压力图像的显着清晰度和改进。我们还报告了一种基于人工智能的算法来确定负载定位的准确性。实验结果表明，该算法在地毯上的位置检测中给出了0.875 cm的均方误差。我们讨论了紧凑且具有成本效益的压力传感器地毯的潜力，它与生活环境和外部世界相结合。