学习评估毫米波心率传感中心跳可观测性
Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing
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中文总结 AI 辅助
针对毫米波雷达非接触式心率感知，提出基于模拟器训练的双任务Transformer（HEAR），联合预测心跳可观测性与心率，实现选择性估计，零样本迁移至真实数据，显著降低心率估计误差。
中文摘要 AI 辅助
基于毫米波雷达的非接触式心率感知需要评估单个测量是否支持可靠的估计。我们研究学习评估心跳可观测性，即获取的相位频谱中心跳分量的可读性，用于选择性心率估计。散射体回波的相干叠加即使在相似的宏观观测几何下也可能抑制该分量，这促使我们直接从获取的测量中评估可观测性。为了在不同可观测性条件下获得训练监督，我们开发了一个可控的多散射体调频连续波（FMCW）模拟器。主导心跳频带峰值与已知心率之间的一致性为每个模拟测量提供了自动可观测性标签。我们提出了HEAR（具有评估可靠性的心跳估计），一个紧凑的双任务Transformer，同时预测可观测性评分和心率。其输入结合了频谱幅度与相对于呼吸基频的频率，为呼吸谐波提供上下文。仅使用模拟观测训练，HEAR零样本迁移到两个公开的真实世界数据集，这些数据集在60和120 GHz下从134名受试者收集。相同的学习评分支持HEAR自身的心率头和多种现有估计器的选择性预测。在120 GHz数据集上，基于评分的选择将心率头的平均绝对误差从全覆盖时的17.9 BPM降低到50%覆盖率时的1.6 BPM。完整流程在边缘设备上实现了50.8毫秒的端到端处理延迟。项目页面：此https URL。
英文摘要
Contactless heart-rate sensing with millimeter-wave (mmWave) radar requires assessing whether individual measurements support reliable estimation. We study learning to assess heartbeat observability, defined as the readability of the heartbeat component in an acquired phase spectrum, for selective heart-rate estimation. Coherent superposition of scatterer returns can suppress this component even under similar macroscopic observation geometry, motivating assessment directly from acquired measurements. To obtain training supervision across different observability conditions, we develop a controllable multi-scatterer frequency-modulated continuous-wave (FMCW) simulator. Agreement between the dominant heartbeat-band peak and the known heart rate provides an automatic observability label for each simulated measurement. We propose HEAR (Heartbeat Estimation with Assessed Reliability), a compact dual-task Transformer that jointly predicts an observability score and heart rate. Its input combines spectral magnitudes with frequencies relative to the respiration fundamental, providing context for respiratory harmonics. Trained solely on simulated observations, HEAR transfers zero-shot to two public real-world datasets collected at 60 and 120 GHz from 134 subjects. The same learned score supports selective prediction with both HEAR's own heart-rate head and multiple existing estimators. On the 120 GHz dataset, score-based selection reduces the HR head's mean absolute error from 17.9 BPM at full coverage to 1.6 BPM at 50% coverage. The complete pipeline achieves an end-to-end processing latency of 50.8 ms on an edge device. Project page: https://yuxuanhu9.github.io/HEAR/.
发表机构
- Fudan University（复旦大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
