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.