# Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing

arXiv: https://arxiv.org/abs/2610.03570 · HTML: https://mlsanity.com/paper/2610.03570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 66 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers](https://mlsanity.com/paper/1805.08206/) (2018)
- [Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2309.06924/) (2023)
- [Facial Video-based Remote Physiological Measurement via Self-supervised Learning](https://mlsanity.com/paper/2210.15401/) (2022)
- [PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement](https://mlsanity.com/paper/2405.06201/) (2024)
- [Contactless Electrocardiogram Monitoring with Millimeter Wave Radar](https://mlsanity.com/paper/2112.06639/) (2021)
- [radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar](https://mlsanity.com/paper/2408.01672/) (2024)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data](https://mlsanity.com/paper/2506.19358/) (2025)
- [WaveMan: mmWave-Based Room-Scale Human Interaction Perception for Humanoid Robots](https://mlsanity.com/paper/2601.07454/) (2026)
- [Noncontact Multi-Point Vital Sign Monitoring with mmWave MIMO Radar](https://mlsanity.com/paper/2411.09201/) (2024)
- [Robust Phantom-Assisted Framework for Multi-Person Localization and Vital Signs Monitoring Using MIMO FMCW Radar](https://mlsanity.com/paper/2501.06755/) (2025)
- [Multidimensional Physiology-Inspired Enhanced Vital Sign Monitoring Using MIMO mmWave Bio-radar](https://mlsanity.com/paper/2510.15278/) (2025)
- [EM-Fall: Embodied mmWave Sensing for Day-and-Night Fall Detection on Humanoid Robots](https://mlsanity.com/paper/2606.11109/) (2026)
- [ActiveVital: Geometry-Aware Embodied Vital Signs Monitoring for Home Healthcare Robots](https://mlsanity.com/paper/2606.30275/) (2026)
- [mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar](https://mlsanity.com/paper/2609.34220/) (2026)

## Abstract (arXiv)

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/.