RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length
  Videos
Bochao Zou Zizheng Guo Xiaocheng Hu Huimin Ma
Remote photoplethysmography (rPPG) is a non-contact method for detecting
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: extracting weak rPPG signals from video segments with large
spatiotemporal redundancy and understanding the periodic patterns of rPPG among
long contexts. This represents a trade-off between computational complexity and
the ability to capture long-range dependencies, posing a challenge for rPPG
that is suitable for deployment on mobile devices. Based on the in-depth
exploration of Mamba's comprehension of spatial and temporal information, this
paper introduces RhythmMamba, an end-to-end Mamba-based method that employs
multi-temporal Mamba to constrain both periodic patterns and short-term trends,
coupled with frequency domain feed-forward to enable Mamba to robustly
understand the quasi-periodic patterns of rPPG. Extensive experiments show that
RhythmMamba achieves state-of-the-art performance with reduced parameters and
lower computational complexity. The proposed RhythmMamba can be applied to
video segments of any length without performance degradation. The codes are
available at https://github.com/zizheng-guo/RhythmMamba.