Implementation of Voxel Selective Ellipse Normalization to Enhance Radar Respiration Estimation in Metallic Chamber

Compared to broader physical activities, detecting nuanced respiratory movements poses a significant challenge in indoor health monitoring systems. While respiratory activity can be conceptualized as periodic chest movements akin to mechanical vibrations, uncontrollable environmental factors often introduce noise into detected radar signals. The clutter in the field of view, especially metallic objects, such as hospital steel beds, degrades the performance of radar physiological monitoring: 1) amplifying noise of multipath effects and 2) misleading the informative localization module. In this article, we propose a preprocessing scheme of Search-Voxel Ellipse Normalization for respiratory detection system, including an ellipse normalization method combined with the fitting-cost voxel selection policy, to improve the respiration detection performance using MIMO frequency modulated continuous wave radar. This article provides an in-depth assessment of the designed system, including a metal-insulated room test, involving ten participants in different postures. The results show notable performance improvements of our proposed ENDTW-MVMD method, especially in lowering the mean absolute error from the best state-of-the-art 0.93–0.75 bpm and stabilization in voxel selection. The proposed approach is thoroughly evaluated against established methods across various dimensions, such as voxel selection, independent performance, frequency estimation, and ablation studies.