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arXiv · 2404.06483

RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement

Bochao Zou·Zizheng Guo·Xiaocheng Hu·Huimin Ma
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Abstract

Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba, a state space model-based method that captures long-range dependencies while maintaining linear complexity. By viewing rPPG as a time series task through the proposed frame stem, the periodic variations in pulse waves are modeled as state transitions. Additionally, we design multi-temporal constraint and frequency domain feed-forward, both aligned with the characteristics of rPPG time series, to improve the learning capacity of Mamba for rPPG signals. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with 319% throughput and 23% peak GPU memory. The codes are available at https://github.com/zizheng-guo/RhythmMamba.

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Bochao Zou, Zizheng Guo, Xiaocheng Hu, Huimin Ma. 2025-02-24. RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement. https://arxiv.org/abs/2404.06483

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