发表机构
Saveetha School of Engineering; Government Kilpauk Medical College and Hospital; SRM Institute of Science and Technology（萨维塔工程学院; 政府基尔帕医学院及医院; SRM科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出PIR跨层框架，结合上下文多臂老虎机等技术实现心血管感知的自适应资源分配，经实验验证其可在满足医疗延迟约束的同时实现低能耗，具备良好性能。
AI 中文摘要
心血管感知系统必须在信号衰减、无线传输丢失、能量受限及边缘计算延迟的情况下，保留临床有用信息。我们提出Physiological Information Reliability（PIR，生理信息可靠性）这一跨层框架，该框架将生理信息价值与无线、能量及计算状态联合表征，并利用上下文多臂老虎机（contextual bandit）自适应调整感知与通信决策。我们将多模态ECG/PPG信号质量估计与生理信息价值、突发删除（burst-erasure）条件下的自适应网络编码层相结合。在受控多种子实验中，PIR-LinUCB展现出极具前景的低能耗工作点，同时满足医疗延迟约束，且相较于固定策略与启发式策略，具备竞争力的生理估计性能。我们分析了由此产生的精度-能量-延迟权衡，并明确了代理PIV估计与模拟通信动态的局限性。这些结果为生理信息感知型资源分配提供了初步计算验证，并为未来临床与真实信道的验证研究提供了方向。
英文摘要
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.
Comments6 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
