Mamba-Based Video Analysis for Blood Pressure Estimation

Blood pressure monitoring is important for overall health assessment, yet traditional cuff-based methods are intrusive and unsuitable for continuous monitoring. This paper proposes a contactless approach for blood pressure estimation from facial videos using a bidirectional Mamba-based architecture with uncertainty quantification. Our method processes 64-frame video segments through a hierarchical 3D convolutional encoder to extract spatiotemporal features, then applies bidirectional state-space modeling to capture temporal dynamics efficiently. The model was evaluated on the Vitals for Vision (V4V) dataset, achieving mean absolute errors of 13.15 mmHg for systolic and 9.56 mmHg for diastolic blood pressure, outperforming prior methods while requiring significantly fewer computational resources than attention-based approaches. While these results do not meet clinical-grade diagnostic standards, they demonstrate the feasibility of contactless blood pressure estimation for non-clinical applications such as wellness monitoring, preliminary health screening, and continuous remote observation, where unobtrusive and computationally efficient monitoring is desirable.