HRMamba: Fusing Luminance Information for Remote Physiological Measurement in Varied Lighting Conditions.

Abstract

Camera-based photoplethysmography (cbPPG) represents a non-invasive technique for capturing physiological parameters through facial videos, enabling the extraction of vital signs such as heart rate, respiration rate, and blood oxygen saturation without direct physical contact. Existing deep learning methods face two core challenges when dealing with cbPPG: firstly, extracting weak PPG signals from video segments with large spatial and temporal redundancy and understanding their periodic patterns in long contexts; secondly, accurately extracting PPG signals in complex lighting environments, especially in low-light conditions. To address these issues, this paper proposes an end-to-end method based on Mamba, named HRMamba. This method employs temporal difference mamba to process temporal signals and combines bidirectional state space to enable Mamba to robustly understand the scene and learn the periodic patterns of PPG. Furthermore, a luminance post-processing module is designed to extract luminance information from the video without enhancing lighting or altering the original video data, and embed it into the PPG signal. Experimental results demonstrate that HRMamba achieves state-of-the-art performance, and the designed luminance post-processing module can be applied in various lighting environments, significantly enhancing the performance in dark environments without degrading the performance in normal light scenes.