PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing (2505.03621v1)

Abstract: Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. LLMs excel at capturing long-range dependencies, offering a potential solution but struggle with the continuous, noise-sensitive nature of rPPG signals due to their text-centric design. To bridge this gap, we introduce PhysLLM, a collaborative optimization framework that synergizes LLMs with domain-specific rPPG components. Specifically, the Text Prototype Guidance (TPG) strategy is proposed to establish cross-modal alignment by projecting hemodynamic features into LLM-interpretable semantic space, effectively bridging the representational gap between physiological signals and linguistic tokens. Besides, a novel Dual-Domain Stationary (DDS) Algorithm is proposed for resolving signal instability through adaptive time-frequency domain feature re-weighting. Finally, rPPG task-specific cues systematically inject physiological priors through physiological statistics, environmental contextual answering, and task description, leveraging cross-modal learning to integrate both visual and textual information, enabling dynamic adaptation to challenging scenarios like variable illumination and subject movements. Evaluation on four benchmark datasets, PhysLLM achieves state-of-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.

PhysLLM: Harnessing LLMs for Cross-Modal Remote Physiological Sensing

The paper under discussion presents an advanced framework, PhysLLM, specifically designed to enhance remote photoplethysmography (rPPG) by integrating LLMs. Remote photoplethysmography is a non-invasive method used to measure physiological signals, such as heart rate, by detecting subtle changes in skin color caused by blood flow, typically captured via facial video sequences. Despite its non-intrusive nature, the traditional rPPG methodologies are often hindered by challenges such as sensitivity to illumination variations and susceptibility to motion artifacts. These issues limit the robustness and accuracy of rPPG in real-world applications.

The innovative approach proposed by PhysLLM focuses on the synergy between LLM's capabilities in long-range dependency modeling and domain-specific rPPG signal processing. However, LLMs traditionally struggled with continuous and noise-sensitive rPPG signals due to their foundational design catering primarily to textual data. To address this inherent mismatch, PhysLLM introduces several key components:

1. Text Prototype Guidance (TPG): This strategy facilitates cross-modal alignment by projecting hemodynamic features into an LLM-interpretable semantic space. This bridging technique reduces the representational gap between continuous physiological signals and linguistic tokens, providing a framework for the integration of physiological data and LLMs.
2. Dual-Domain Stationary (DDS) Algorithm: PhysLLM incorporates this algorithm to enhance signal stability by adaptively re-weighting features in both the time and frequency domains. DDS addresses rPPG's signal instability, enabling more consistent physiological measurements across varying conditions.
3. Task-Specific Cues: The framework systematically integrates physiological priors such as environmental context, physiological statistics, and task descriptions. Cross-modal learning mechanisms leverage these task-specific cues to enhance the adaptability of the model in scenarios with challenging variables, including dynamic lighting conditions and subject movements.

The evaluation of PhysLLM across four benchmark datasets demonstrates its superior performance in terms of accuracy and robustness, confirming its state-of-the-art status. Specifically, notable improvements were seen in its ability to generalize across datasets with varying conditions, a frequent challenge in rPPG tasks.

For experienced researchers, the implications of PhysLLM are manifold. Practically, it suggests a pathway towards more robust and widely applicable non-contact physiological monitoring systems, a critical need in domains such as telehealth and wellness monitoring. Theoretically, it opens up prospects for further exploration into cross-modal frameworks that utilize LLMs in traditionally non-textual domains by exploiting semantic alignments.

In the future, the development of adaptive cross-modal strategies like those introduced in PhysLLM could pave the way for more generalized AI systems that capitalize on the diverse data modalities available in complex real-world settings. Additionally, the continual refinement of LLM architectures to accommodate non-textual data without significant fine-tuning can substantially broaden their applicability, reducing deployment times and conserving computational resources. Such advancements align with ongoing efforts in AI development to create more versatile, efficient models that are capable of performing across a range of tasks while leveraging the strengths of multimodal data inputs.