{"ID":2843299,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08071","arxiv_id":"2511.08071","title":"Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast","abstract":"Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labeled signals for supervised training. To overcome these limitations, we propose the first unsupervised framework for radar-based heartbeat sensing via Augmented Pseudo-Label and Noise Contrast (Radar-APLANC). We propose to use both the heartbeat range and noise range within the radar range matrix to construct the positive and negative samples, respectively, for improved noise robustness. Our Noise-Contrastive Triplet (NCT) loss only utilizes positive samples, negative samples, and pseudo-label signals generated by the traditional radar method, thereby avoiding dependence on expensive ground-truth physiological signals. We further design a pseudo-label augmentation approach featuring adaptive noise-aware label selection to improve pseudo-label signal quality. Extensive experiments on the Equipleth dataset and our collected radar dataset demonstrate that our unsupervised method achieves performance comparable to state-of-the-art supervised methods. Our code, dataset, and supplementary materials can be accessed from https://github.com/RadarHRSensing/Radar-APLANC.","short_abstract":"Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labe...","url_abs":"https://arxiv.org/abs/2511.08071","url_pdf":"https://arxiv.org/pdf/2511.08071v1","authors":"[\"Ying Wang\",\"Zhaodong Sun\",\"Xu Cheng\",\"Zuxian He\",\"Xiaobai Li\"]","published":"2025-11-11T10:14:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.HC\",\"eess.SP\"]","methods":"[]","has_code":false,"code_links":[{"ID":607198,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2843299,"paper_url":"https://arxiv.org/abs/2511.08071","paper_title":"Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast","repo_url":"https://github.com/RadarHRSensing/Radar-APLANC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}