Deformation-Aware Observation Modeling for Radar-Based Human Sensing via 3D Scan-Depth Sequence Fusion

Non-contact radar-based human sensing is often interpreted using simplified motion assumptions. However, respiration induces non-rigid surface deformation of the human body that impacts electromagnetic wave scattering and can degrade the robustness of measurements. To address this, we propose a surface-deformation-aware observation model for radar-based human sensing that fuses static high-resolution three-dimensional scanner measurements with temporal depth camera data to represent time-varying human surface geometry. Non-rigid registration using the coherent point drift algorithm is employed to align a static template with dynamic depth frames. Frame-wise electromagnetic scattering is subsequently computed using the physical optics approximation, allowing the reconstruction of intermediate-frequency radar signals that emulate radar observations. Validation against experimental radar data demonstrated that the proposed model exhibited greater robustness than a depth-sequence-only model under low-signal-quality conditions involving complex surface dynamics and multiple reflective sites. For two participants, the proposed model achieved higher Pearson correlation coefficients of 0.943 and 0.887 between model-derived and experimentally measured displacement waveforms, compared with 0.868 and 0.796 for the depth-sequence-only model. Furthermore, in a favorable case characterized by a single relatively-stationary reflective site, the proposed method achieved a correlation coefficient of 0.789 between model-derived and experimentally measured in-phase-quadrature magnitude variations. These results suggest that our sensor-fusion-based deformation-aware observation modeling can realistically reproduce radar observations and provide physically grounded insights into the interpretation of radar measurement variations.

Accurate Radar-Based Heartbeat Measurement Using Higher Harmonic Components

This study proposes a radar-based heartbeat measurement method that uses the absolute value of the second derivative of the complex radar signal, rather than its phase, and the variational mode extraction method, which is a type of mode decomposition algorithm. We show that the proposed second-derivative-based approach can amplify the heartbeat component in radar signals effectively and also confirm that use of the variational mode extraction method represents an efficient way to emphasize the heartbeat component amplified via the second-derivative-based approach. We demonstrate estimation of the heart interbeat intervals using the proposed approach in combination with the topology method, which is an accurate interbeat interval estimation method. The performance of the proposed method is evaluated quantitatively using data obtained from eleven participants that were measured using a millimeter-wave radar system. When compared with conventional methods based on the phase of the complex radar signal, our proposed method can achieve higher accuracy when estimating the heart interbeat intervals; the correlation coefficient for the proposed method was increased by 0.20 and the root-mean-square error decreased by 23%.

Complex Number Assignment in the Topology Method for Heartbeat Interval Estimation Using Millimeter-Wave Radar

The topology method is an algorithm for accurate estimation of instantaneous heartbeat intervals using millimeter-wave radar signals. In this model, feature points are extracted from the skin displacement waveforms generated by heartbeats and a complex number is assigned to each feature point. However, these numbers have been assigned empirically and without solid justification. This study used a simplified model of displacement waveforms to predict the optimal choice of the complex number assignments to feature points corresponding to inflection points, and the validity of these numbers was confirmed using analysis of a publicly available dataset.