MIRO: Multi-radar Identity and Ranging for Occupational Safety

TL;DR

MIRO is a privacy-preserving multi-radar system that tracks workers and measures airborne particulate matter in industrial settings, providing accurate exposure data without visual identification.

Contribution

The paper introduces MIRO, combining multi-radar millimeter-wave re-identification with PM sensing, and a GAN-based view adaptation network for robust worker tracking.

Findings

Achieved 90.4% re-ID F1-score in lab experiments

Demonstrated reliable worker-specific PM exposure estimation in field trials

Validated robustness of the system in rural stone-cutting yards

Abstract

Occupational exposure to airborne particulate matter (PM) poses a severe health risk in open industrial workspaces such as stonecutting yards. Conventional monitoring solutions such as wearable PM sensors and camera-based tracking are impractical due to discomfort, maintenance issues, and privacy concerns. We present MIRO, a privacy-preserving framework that integrates continuous PM sensing with a multi-radar millimeter-wave (mmWave) re-identification (re-ID) backbone. A distributed network of PM sensors captures localized pollutant concentrations, while spatially overlapping mmWave radars track and re-associate workers across viewpoints without relying on visual cues. To ensure identity consistency across radars, we introduce a GAN-based view adaptation network that compensates for azimuthal distortions in range-Doppler (RD) signatures, combined with correlation-based cross-radar matching. In controlled laboratory experiments, our system achieves a re-ID F1-score of 90.4% and a mean Structural Similarity Index Measure (SSIM) of 0.70 for view adaptation accuracy. Field trials in rural stone-cutting yards further validate the system's robustness, demonstrating reliable worker-specific PM exposure estimation.