PhysAgent: A Multi-Agent Framework for Reliable Remote Heart Rate Estimation
Quick Answer
PhysAgent is a multi-agent framework that enhances remote heart rate estimation by verifying outputs from multiple estimators using a lightweight MLLM, Qwen3-VL-4B.
Quick Take
It improves fusion stability and reliability across various datasets, outperforming traditional direct prediction methods. The framework addresses issues of irreproducibility and physiological inconsistency seen in existing rPPG techniques.
Key Points
- PhysAgent uses a verification framework for heart rate estimation.
- It leverages Qwen3-VL-4B for reasoning over video conditions and signal reliability.
- Experimental results show improved stability across multiple public rPPG benchmarks.
- The framework avoids irreproducibility issues found in direct MLLM predictions.
- Code for PhysAgent will be released soon for public access.
DeepSignal Analysis
What happened
PhysAgent is a framework designed to enhance remote heart rate estimation by verifying outputs from multiple estimators. It utilizes a lightweight MLLM, Qwen3-VL-4B, to improve the reliability and stability of heart rate predictions across various datasets, addressing issues found in traditional rPPG methods.
Key evidence
- PhysAgent employs a multi-agent candidate-verification framework to resolve conflicts among different estimators, rather than relying on a single model for predictions.
- The framework uses the Qwen3-VL-4B MLLM to assess video conditions and signal reliability, which helps in verifying physiological hypotheses.
- Experimental results indicate that PhysAgent enhances fusion stability and reliability across multiple public rPPG benchmarks, mitigating issues of irreproducibility and physiological inconsistency.
Why it matters
The development of PhysAgent is significant as it addresses critical challenges in remote heart rate estimation, such as susceptibility to motion and lighting changes. By improving the reliability of heart rate measurements, it has potential applications in telemedicine and health monitoring, where accurate data is crucial.
Paper Resources
Source Excerpt
Remote photoplethysmography (rPPG) enables non-contact heart-rate estimation from facial videos, but its weak physiological signal is easily corrupted by motion, illumination changes, occlusion, skin-appearance variation, and device noise. Existing rPPG methods typically rely on a single model to directly predict heart rate or recover pulse waveforms, while different strong estimators may produce conflicting yet individually plausible candidates for the same video. To resolve these conflicts, we
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
ProMoE-FL introduces a Prototype-conditioned Mixture-of-Experts framework for multimodal federated learning, effectively addressing missing modalities. It outperforms existing methods on four chest X-ray datasets, demonstrating superior feature synthesis capabilities in both homogeneous and heterogeneous settings.