Explaining RhythmFormer: A Systematic XAI Analysis of Periodic Sparse Attention for Remote Photoplethysmography
arXiv:2606.13839v1 Announce Type: cross Abstract: Remote photoplethysmography (rPPG) transformers achieve low heart-rate error on benchmarks, yet their decisions remain opaque--a growing concern as rPPG moves toward clinical heart rate estimation. Existing rPPG XAI is dominated by qualitative heatmap inspection without quantitative faithfulness metrics or physiology-grounded validation, leaving a gap between visual plausibility and auditable evidence. We address this gap. First, we adapt four attribution methods (raw attention, rollout, flow, Beyond Intuition) to RhythmFormer's bi-level routin
The paper addresses the growing need for explainable AI (XAI) in critical applications like remote photoplethysmography (rPPG), as these technologies move from research benchmarks towards real-world clinical deployment.
Ensuring the transparency and trustworthiness of AI systems, especially in healthcare, is crucial for regulatory approval, clinical adoption, and preventing unintended consequences, directly addressing the 'black box' problem.
This work refines the methodologies for quantitatively evaluating and explaining AI decisions in a physiology-grounded manner, moving beyond qualitative assessments and setting a new standard for auditable AI in health tech.
- · AI healthcare developers
- · Medical regulatory bodies
- · Patients
- · Explainable AI research community
- · Opaque AI systems in healthcare
- · Developers solely relying on qualitative XAI methods
Increased trust and faster adoption of AI-driven remote health monitoring solutions due to enhanced explainability.
New regulatory requirements for quantitative XAI metrics in clinical AI, pushing developers to integrate these methods from the outset.
The methodology could be generalized to other sensitive AI applications beyond healthcare, fostering broader explainable AI standards across industries.
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Read at arXiv cs.AI