Pressure Estimation

Pressure estimation research focuses on accurately determining pressure distribution from various data sources, aiming to improve diverse applications from leak detection in water networks to robotic manipulation and healthcare. Current research employs diverse approaches, including graph neural networks for analyzing network structures and inferring pressures from sensor data, and deep learning models trained on visual data (RGB or infrared) to estimate pressure from images of deformable surfaces like soft grippers or human bodies. These advancements have significant implications for optimizing infrastructure management, enhancing robotic dexterity, and improving patient care through non-invasive pressure monitoring.