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Physiological Information Reliability: Cross-Layer Adaptive Resource Allocation for Cardiovascular Sensing

Navaneeth Krishnan Kamalakannan, Janakiraman Kamalakannan, Harinisri Velmurugan

Latestcs.CLcs.LGcs.AIcs.CV
arXiv ID
2609.00435 v1
Submitted
2026-08-31

Abstract

Cardiovascular sensing systems must preserve clinically useful information despite signal degradation, wireless losses, energy constraints, and edge-computation latency. We introduce Physiological Information Reliability (PIR), a cross-layer framework that represents physiological information value jointly with wireless, energy, and computation states and uses a contextual bandit to adapt sensing and communication decisions. We integrate multimodal ECG/PPG signal-quality estimation with physiological information value and an adaptive network-coding layer under burst-erasure conditions. Across controlled multiseed experiments, PIR-LinUCB demonstrates a promising low-energy operating point while maintaining medical latency constraints and competitive physiological estimation performance relative to fixed and heuristic policies. We analyze the resulting accuracy-energy-latency trade-offs and identify limitations of proxy PIV estimation and simulated communication dynamics. These results provide an initial computational demonstration of physiological-information-aware resource allocation and motivate future clinical and real-channel validation.

Comment: 6 pages, 4 figures, 3 tables. Submitted to the Machine Learning for Health (ML4H) 2026 Symposium, Findings Track. Code and experimental artifacts: https://github.com/ka-cyber/PIR-Framework

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