Eulerian Magnification of Multi-Modal RGB-D Video for Heart Rate Estimation

Abstract

Eulerian Video Magnification (EVM) has been shown to be highly effective for non-contact, unobtrusive, and non-invasive patient heart rate (HR) estimation systems. EVM is typically applied to RGB video to amplify minute changes in skin color due to varying blood flow, thereby estimating HR. Previous methods require knowledge of the expected HR to optimize the passband to be amplified via EVM. Furthermore, most EVM methods operating on natural light video often fail in low-light environments. This paper proposes a multi-modal selective passband search approach, utilizing predefined EVM passbands, and the use of intelligent data fusion of the three different modalities provided by the Intel RealSense RGB-D camera. We demonstrate the effectiveness of using the color, depth, and near-infrared streams to obtain a consensus HR estimate under various lighting conditions and subject poses. Results indicate that the fusion of HR estimates acquired from each modality is effective and robust to environmental conditions.