Pain Detection

Automated pain detection is a rapidly evolving field aiming to objectively assess pain levels, overcoming limitations of subjective self-reporting. Current research focuses on developing robust models using diverse data sources, including facial expressions analyzed via computer vision (employing convolutional neural networks, vision transformers, and diffusion models), physiological signals like blood volume pulse (analyzed with machine learning algorithms like XGBoost), and motion capture data (processed with lightweight, spatially-focused attention networks). These advancements hold significant promise for improving patient care, particularly in situations with communication barriers or limited medical resources, by enabling more accurate pain assessment and facilitating timely interventions.