{"ID":2856743,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10542","arxiv_id":"2510.10542","title":"Data Integration Using Multivariate Mode Decomposition for Physiological Sensing with Multiple Millimeter-Wave Radar Systems","abstract":"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.","short_abstract":"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 su...","url_abs":"https://arxiv.org/abs/2510.10542","url_pdf":"https://arxiv.org/pdf/2510.10542v1","authors":"[\"Kimitaka Sumi\",\"Takuya Sakamoto\"]","published":"2025-10-12T10:51:31Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}