Data Integration Using Multivariate Mode Decomposition for Physiological Sensing with Multiple Millimeter-Wave Radar Systems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915548789473280 |
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| author | Sumi, Kimitaka Sakamoto, Takuya |
| author_facet | Sumi, Kimitaka Sakamoto, Takuya |
| contents | This study proposes a multi-radar system for non-contact physiological sensing across arbitrary body orientations. In integrating signals obtained from different radar viewpoints, we adopt a multivariate variational mode decomposition method to extract the common respiratory component. Experiments conducted with six subjects under varying distances and orientations demonstrate that, compared with a single-radar setup, the proposed system reduced the root mean square error of the respiratory interval by 35.5%, decreased the mean absolute error of the respiratory rate by 30.8%, and improved accuracy by 9.4 percentage points. These results highlight that combining multiple radar viewpoints with signal integration enables stable respiratory measurement regardless of body orientation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Integration Using Multivariate Mode Decomposition for Physiological Sensing with Multiple Millimeter-Wave Radar Systems Sumi, Kimitaka Sakamoto, Takuya Signal Processing This study proposes a multi-radar system for non-contact physiological sensing across arbitrary body orientations. In integrating signals obtained from different radar viewpoints, we adopt a multivariate variational mode decomposition method to extract the common respiratory component. Experiments conducted with six subjects under varying distances and orientations demonstrate that, compared with a single-radar setup, the proposed system reduced the root mean square error of the respiratory interval by 35.5%, decreased the mean absolute error of the respiratory rate by 30.8%, and improved accuracy by 9.4 percentage points. These results highlight that combining multiple radar viewpoints with signal integration enables stable respiratory measurement regardless of body orientation. |
| title | Data Integration Using Multivariate Mode Decomposition for Physiological Sensing with Multiple Millimeter-Wave Radar Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.10542 |