Heartbeat observability improves millimeter-wave heart-rate sensing accuracy
Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing
Artificial Intelligence
Summary
Measuring heart rate without physical contact using millimeter-wave radar can be tricky because some signals are hard to detect clearly. The authors created a smart system named HEAR to decide when the heartbeat signal is strong enough to trust before estimating the heart rate. They trained it using simulated data and found it also works well on real-world measurements from many people. This approach helps reduce big errors in heart-rate readings and runs quickly on small devices.
What this means in practice
- •For wearable device engineers: Improve accuracy of contactless heart-rate monitoring by selecting measurements with reliable heartbeat signals using HEAR's observability scoring.
- •For healthcare hardware teams: Integrate selective heart-rate estimation with low latency on edge devices for real-time patient monitoring without physical sensors.$Commercial implications: Enables development of more reliable non-contact heart-rate monitors for hospitals and clinics, reducing the need for wearable sensors.
Authors
Yuxuan Hu, Shilin Shan, Jianfei Yang, Feng Xu
Abstract
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/.