Pain Recognition

Pain recognition research aims to develop objective and reliable methods for assessing pain, particularly in individuals unable to self-report. Current efforts focus on leveraging deep learning, including transformer networks and convolutional neural networks, to analyze multimodal data such as facial expressions from video, physiological signals (e.g., blood volume pulse), and movement data from inertial sensors. These advancements address limitations of traditional self-report methods and hold significant promise for improving pain management and clinical decision-making across diverse populations, including those with neurological conditions. The development of synthetic datasets is also crucial to address ethical and data scarcity challenges in this field.