Magnifying Facial Micro-movements for Cognitive Evaluation

Objective cognitive assessment is critical for the early detection and management of cognitive decline. The Mini-Mental State Examination (MMSE) is a widely used tool for this purpose, but it requires face-to-face interaction and manual scoring by clinicians. Recent advances in computer vision and deep learning offer the potential to automate and enhance the accuracy of such assessments. This study presents a novel deep learning model that integrates multimodal data captured during cognitive testing sessions on a tablet. By focusing on facial movements, which are captured and magnified through a pre-processing pipeline, the model classifies inputs into categories corresponding to MMSE scores. Our results show a significant correlation between facial movements and MMSE, suggesting the feasibility of using automated video analysis as a reliable proxy for cognitive assessment.