提出雷达心率监测的谱图增强方法，解决数据少导致的模型性能差问题。
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components
- 在频谱图中注入零值，增强模型对细微心电信号的感知能力。
- 在有限数据下提升心率检测与心电图重建准确率16.20%。
- 适用于雷达生命体征监测中的分类与回归任务，适合医疗无接触监测研究者。
基于雷达的健康监测可通过非接触方式获取精准生命体征，但数据稀缺限制了深度学习方法的发展。数据增强常用于扩充数据集，但多数方法仅适用于分类任务。为支持回归任务，本文提出一种名为Horcrux的频谱图增强方法，用于雷达基心脏特征监测（如心跳检测、心电图重构），同时兼顾分类与回归任务。该方法在保持增强后频谱图与原始真实生命体征一致的前提下，增加输入样本多样性。此外，通过在特定区域注入零值，提升模型对微弱心脏特征的敏感性，从而改善小样本下的性能。实验表明，Horcrux在心脏监测任务上整体提升16.20%，且具备扩展至其他基于频谱图任务的潜力。代码将在发表后公开。
原文摘要 · Abstract (English)
Radar-based wellness monitoring is becoming an effective measurement to provide accurate vital signs in a contactless manner, but data scarcity retards the related research on deep-learning-based methods. Data augmentation is commonly used to enrich the dataset by modifying the existing data, but most augmentation techniques can only couple with classification tasks. To enable the augmentation for regression tasks, this research proposes a spectrogram augmentation method, Horcrux, for radar-based cardiac feature monitoring (e.g., heartbeat detection, electrocardiogram reconstruction) with both classification and regression tasks involved. The proposed method is designed to increase the diversity of input samples while the augmented spectrogram is still faithful to the original ground truth vital sign. In addition, Horcrux proposes to inject zero values in specific areas to enhance the awareness of the deep learning model on subtle cardiac features, improving the performance for the limited dataset. Experimental result shows that Horcrux achieves an overall improvement of 16.20% in cardiac monitoring and has the potential to be extended to other spectrogram-based tasks. The code will be released upon publication.
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