Key result
Contactless radar-LLM framework outperforms standard baselines, achieving ~90% accuracy for arrhythmia diagnosis.
Why the study?
Existing radar monitoring solutions for arrhythmia fail to detect concurrent arrhythmias and lack clinical interpretability, limiting their diagnostic utility.
Does the READ framework improve contactless arrhythmia diagnosis compared to state-of-the-art baselines in patients?
Does the READ framework improve contactless arrhythmia diagnosis compared to state-of-the-art baselines in patients?
The READ framework demonstrates high accuracy and interpretability for contactless arrhythmia diagnosis using mmWave radar and LLMs.
Should not yet alter arrhythmia monitoring practice; leaves open prospective validation of radar-LLM interpretability.
Arrhythmia poses a severe health risk, necessitating long-term monitoring to prevent complications like stroke. However, existing radar monitoring solutions fail to detect concurrent arrhythmias and lack clinical interpretability. In this paper, we bridge the gap by proposing READ (Radar Evidence-based Arrhythmia Diagnosis). Unlike previous “black-box” approaches, READ integrates mmWave radar with Large Language Models (LLMs) to deliver transparent, evidence-based diagnoses. However, training such a reasoning-capable model is hindered by the scarcity of mmWave datasets with detailed diagnostic annotations. To tackle this challenge, we first pretrain on large-scale ECG data to learn robust physiological patterns and then transfer this knowledge to radar domain using our proposed Cross-Physics Knowledge Distillation (CPKD) framework. Furthermore, we design a Morphological Grouping Tokenizer and a Morphology-Anchored Clinical Reasoning module, which parse raw signals into semantic tokens to guide step-by-step diagnostic inference. Empirical evaluation on a real-world dataset of 205 patients, collected in collaboration with medical institutions, demonstrates READ 's superior performance, achieving a 90.12% exact match ratio and a 0.9625 F 1 score. Our framework significantly outperforms state-of-the-art baselines while providing interpretable clinical reports.
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Lyu et al. (2026) studied Arrhythmia (n=205). READ (Radar Evidence-based Arrhythmia Diagnosis) vs. state-of-the-art baselines was evaluated on exact match ratio and F1 score. The READ framework, integrating mmWave radar with LLMs, achieved a 90.12% exact match ratio and a 0.9625 F1 score for contactless arrhythmia diagnosis, outperforming state-of-the-art baselines.
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