# Memory-efficient Low-latency Remote Photoplethysmography through Temporal-Spatial State Space Duality

arXiv: https://arxiv.org/abs/2504.01774 · HTML: https://mlsanity.com/paper/2504.01774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024)
- [PhysMamba: Efficient Remote Physiological Measurement with SlowFast Temporal Difference Mamba](https://mlsanity.com/paper/2409.12031/) (2024)
- [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023)
- [CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement](https://mlsanity.com/paper/2502.13624/) (2025)
- [The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention](https://mlsanity.com/paper/2010.07770/) (2020)
- [Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes](https://mlsanity.com/paper/2409.19223/) (2024)
- [A Plug-and-Play Temporal Normalization Module for Robust Remote Photoplethysmography](https://mlsanity.com/paper/2411.15283/) (2024)
- [VitalVideos-Europe: A dataset of face videos with PPG and blood pressure ground truths](https://mlsanity.com/paper/2306.11891/) (2023)
- [Camera Measurement of Blood Oxygen Saturation](https://mlsanity.com/paper/2503.01699/) (2025)

## Built on by (most cited first, 2 of 2)

- [PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring](https://mlsanity.com/paper/2507.19172/) (2025)
- [MS-rPPG: Multi-spectral State Space Model for Remote Photoplethysmography in Driver Monitoring Systems](https://mlsanity.com/paper/2606.21115/) (2026)

## Abstract (arXiv)

Remote photoplethysmography (rPPG), enabling non-contact physiological monitoring through facial light reflection analysis, faces critical computational bottlenecks as deep learning introduces performance gains at the cost of prohibitive resource demands. This paper proposes ME-rPPG, a memory-efficient algorithm built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time constraints. Leveraging a transferable state space, ME-rPPG efficiently captures subtle periodic variations across facial frames while maintaining minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. Achieving cross-dataset MAEs of 5.38 (MMPD), 0.70 (VitalVideo), and 0.25 (PURE), ME-rPPG outperforms all baselines with improvements ranging from 21.3% to 60.2%. Our solution enables real-time inference with only 3.6 MB memory usage and 9.46 ms latency -- surpassing existing methods by 19.5%-49.7% accuracy and 43.2% user satisfaction gains in real-world deployments. The code and demos are released for reproducibility on https://health-hci-group.github.io/ME-rPPG-demo/.