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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.05353 |
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| _version_ | 1866911571481985024 |
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| author | Xu, Chendong Yao, Shuai Bao, Haoying Ma, Chiyuan Wu, Qisong |
| author_facet | Xu, Chendong Yao, Shuai Bao, Haoying Ma, Chiyuan Wu, Qisong |
| contents | Radar-based non-contact respiration rate (RR) measurement has become increasingly popular due to its convenience, non-intrusiveness, and low cost. However, it is still quite challenging to accurately acquire vital signs estimation in complex measurement scenarios with large-scale random body movements (RBM), particularly for RR estimation due to strong low-frequency interferences. To cope with the RBM challenge in RR estimation, we propose a novel two-stage RR estimation scheme involving detecting the portion of signals, called as quasi-stationary slices, exhibiting the quasi-stationary pattern. At the detection stage, an enhanced deep neural network framework incorporating the dynamic snake convolution is exploited to detect the quasi-stationary slices in the micro-Doppler spectra. At the estimation stage, we mitigate RBM interferences and achieve accurate RR estimation by only using the portion of ridges consistent with the location of detected quasi-stationary slice. Extensive experimental results demonstrate that our proposed scheme can accurately detect quasi-stationary slices under normal scenarios with large-scale RBM, thereby reducing the error of subsequent RR estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05353 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quasi-stationary Slice Detection-Based Robust Respiration Rate Estimation under Large-scale Random Body Movement Xu, Chendong Yao, Shuai Bao, Haoying Ma, Chiyuan Wu, Qisong Signal Processing Radar-based non-contact respiration rate (RR) measurement has become increasingly popular due to its convenience, non-intrusiveness, and low cost. However, it is still quite challenging to accurately acquire vital signs estimation in complex measurement scenarios with large-scale random body movements (RBM), particularly for RR estimation due to strong low-frequency interferences. To cope with the RBM challenge in RR estimation, we propose a novel two-stage RR estimation scheme involving detecting the portion of signals, called as quasi-stationary slices, exhibiting the quasi-stationary pattern. At the detection stage, an enhanced deep neural network framework incorporating the dynamic snake convolution is exploited to detect the quasi-stationary slices in the micro-Doppler spectra. At the estimation stage, we mitigate RBM interferences and achieve accurate RR estimation by only using the portion of ridges consistent with the location of detected quasi-stationary slice. Extensive experimental results demonstrate that our proposed scheme can accurately detect quasi-stationary slices under normal scenarios with large-scale RBM, thereby reducing the error of subsequent RR estimation. |
| title | Quasi-stationary Slice Detection-Based Robust Respiration Rate Estimation under Large-scale Random Body Movement |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.05353 |