Pressure Data

Pressure data analysis is a rapidly evolving field focusing on extracting meaningful information from pressure measurements across diverse applications, from human activity recognition to industrial process monitoring. Current research emphasizes the development of machine learning models, including convolutional neural networks and other deep learning architectures, to analyze pressure data, often coupled with techniques like contrastive learning or physics-informed approaches to improve accuracy and efficiency, even with limited data. These advancements are significantly impacting various sectors, enabling improved healthcare diagnostics (e.g., infant neuromotor assessment, heart rate monitoring), more efficient industrial processes (e.g., leakage detection in water systems, combustion engine knock detection), and the creation of more sophisticated human-computer interfaces (e.g., smart textiles and beds).