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

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.

6 pages, 4 figures, 3 tables. Submitted to the Machine Learning for Health (ML4H) 2026 Symposium, Findings Track. Code and experimental artifacts: this https URL

Signal Processing (eess.SP); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2609.00435 [eess.SP]

(or arXiv:2609.00435v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.00435 arXiv-issued DOI via DataCite (pending registration)