Beyond Pain Intensity Scores: A Systematic Review of Multimodal Digital Neurophysiological Sensors for Objective Pain Assessment in the Clinical Application

Abstract

People have been looking for an objective measure of chronic pain for decades. Current objective measures can be achieved by correlating patients’ self-report with physiological signal features, but such measures often yield inconsistent results due to individual variability. While existing approaches heavily rely on pain intensity, this study reviewed literatures in differentiating pain types and pain locations using neurophysiological sensors. Results showed that using modalities like EEG and fNIRS, high classification accuracy for specific stimuli can be achieved. However, most existing datasets lack integration of both neurological and peripheral physiological signals and do not label multimodal pain dimensions, such as type, intensity, and location. The review highlights an urgent need for datasets that combine these modalities to support objective pain assessment. Future research should also prioritize multidimensional labeling, population diversity, and validation frameworks to advance personalized pain management and rehabilitation workflows.