inb4

Pain assesment remains a challenge due to its subjective nature and multidimensional characteristics. This study introduces a novel approach utilizing a multimodal model embedding, with inputs from a comprehensive anamnesis and sensor data, to estimate pain levels. By leveraging the Dimensionally Insensitive Euclidean Metric (DIEM), we ensure robust integration of heterogenous data streams, allowing for a reliable and scalable method to quantify pain in clinical and research settings.