Physiology-Informed Multivariate Variational Mode Decomposition for Orientation-Robust Sensing Using Distributed Millimeter-Wave Radar Systems

Abstract

This study proposes a robust non-contact physiological sensing method using distributed radar systems to address the challenges arising from varying body orientations. Conventional physiological sensing methods using a single radar system suffer from performance degradation caused by variations in the relative geometry between the radar and the human body, particularly those caused by changes in body orientation. To help radar systems overcome this limitation, we propose a multivariate physiological variational mode decomposition method that extracts common respiratory and heart rates across multiple displacements of various body parts acquired from distributed radar systems. The proposed method incorporates harmonic constraints and a gap component tailored to the intrinsic nature of physiological signals into the model, thereby improving both estimation accuracy and robustness. Simultaneous measurements using four radar systems were performed on six participants under different participant-position and body-orientation conditions. Using the proposed method, we achieved success rates exceeding 90% for respiration and heartbeat estimation, which represent improvements of 19.8 and 25.3 percentage points, respectively, over rates using a conventional method based on a single radar system. Furthermore, we conducted simultaneous measurements of 16 participants using two radar systems in a multi-person scenario and successfully estimated both respiratory and heart rates with over 85% success rates. Our work contributes to the realization of practical radar-based non-contact physiological sensing by establishing a data fusion framework for distributed radar systems.

14 pages, 9 figures, 4 tables. This work is going to be submitted to the IEEE for possible publication