Performance Evaluation of Flexible Optical Pressure Sensors Using Inverse Model-Based Pressure Mapping

This work presents a signal processing and reconstruction system developed for a flexible optical pressure 2D mapping sensor. The sensor consists of a two-dimensional grid of polyurethane optical fibers (PU-OFs) embedded in polydimethylsiloxane (PDMS), which acts as the input device for acquiring light intensity changes caused by external surface-applied pressure. In this study, we propose a system to process these signals through an inverse model based on the Moore–Penrose pseudoinverse for spatial localization, along with a point-specific pressure estimation model to infer the magnitude of the applied force, which is then used to generate quantitative pressure maps. Experimental results show the system’s overall performance, robustness, and repeatability across multiple pressure levels and locations. In most cases, localization errors remain below 5 mm, while pressure estimation errors are around 5 mmHg when the pressure is correctly localized. Performance metrics, such as recall, specificity, and precision, support the system’s ability to detect, localize, and reconstruct pressure events with consistent reliability. These results establish the viability of the proposed methodology for potential integration into low-cost and flexible optical fiber-based 2D pressure monitoring systems for biomedical applications.