MIRO: Multi-radar Identity and Ranging for Occupational Safety

2026-03-08

AI Summary

This study addresses the health risks posed by airborne particulate matter (PM) exposure to workers in open industrial environments, where existing wearable sensors and vision-based tracking methods suffer from user discomfort, high maintenance demands, and privacy concerns. To overcome these limitations, this work proposes a privacy-preserving, vision-free approach that integrates distributed PM sensing with multiple millimeter-wave radars to enable cross-view worker re-identification and accurate individual exposure assessment. The authors innovatively design a GAN-based view-adaptive network to correct azimuth-induced distortions in range-Doppler features and enhance cross-radar re-identification through correlation-based matching. Experimental results demonstrate a re-identification F1-score of 90.4% and a view-adaptive SSIM of 0.70 in laboratory settings, while field tests at a stone processing site confirm the system’s effectiveness and reliability in real-world individual PM exposure evaluation.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• occupational safety
• airborne particulate matter
• worker tracking
• privacy-preserving monitoring
• industrial exposure

Innovation

• mmWave radar
• re-identification
• GAN-based view adaptation
• privacy-preserving sensing
• occupational exposure monitoring