Radar-APLANC：基于增强伪标签与噪声对比的无监督雷达心跳感知
Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast
AI总结：
针对传统雷达心跳感知因噪声性能下降、基于学习的方法需昂贵标注的问题，提出首个无监督框架Radar-APLANC，通过增强伪标签与噪声对比构建样本并设计NCT损失，性能可媲美有监督方法。
摘要：
调频连续波（FMCW）雷达可通过测量胸部微小振动实现非接触式心跳感知，但传统雷达心跳感知方法因噪声导致性能下降。基于学习的雷达方法噪声鲁棒性更强，但需昂贵的标注信号进行监督训练。为克服这些局限，我们提出首个无监督雷达心跳感知框架Radar-APLANC，通过增强伪标签与噪声对比实现。我们利用雷达距离矩阵中的心跳范围和噪声范围分别构建正样本和负样本，以提升噪声鲁棒性。我们的噪声对比三元组（NCT）损失仅使用传统雷达方法生成的正样本、负样本和伪标签信号，避免依赖昂贵的真实生理信号。我们进一步设计自适应噪声感知标签选择的伪标签增强方法，以提升伪标签信号质量。在Equipleth数据集和我们收集的雷达数据集上的大量实验表明，我们的无监督方法性能可与最先进的有监督方法媲美。代码、数据集和补充材料可从https://github.com/RadarHRSensing/Radar-APLANC获取。
英文摘要：
Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labeled signals for supervised training. To overcome these limitations, we propose the first unsupervised framework for radar-based heartbeat sensing via Augmented Pseudo-Label and Noise Contrast (Radar-APLANC). We propose to use both the heartbeat range and noise range within the radar range matrix to construct the positive and negative samples, respectively, for improved noise robustness. Our Noise-Contrastive Triplet (NCT) loss only utilizes positive samples, negative samples, and pseudo-label signals generated by the traditional radar method, thereby avoiding dependence on expensive ground-truth physiological signals. We further design a pseudo-label augmentation approach featuring adaptive noise-aware label selection to improve pseudo-label signal quality. Extensive experiments on the Equipleth dataset and our collected radar dataset demonstrate that our unsupervised method achieves performance comparable to state-of-the-art supervised methods. Our code, dataset, and supplementary materials can be accessed from https://github.com/RadarHRSensing/Radar-APLANC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
