@inproceedings{10.1145/3774906.3802761,
author = {Halder, Tirthankar and Sen, Argha and Pradhan, Swadhin and Sen, Rijurekha and Chakraborty, Sandip},
title = {MIRO: Multi-Radar Identity and Ranging for Occupational Safety},
year = {2026},
isbn = {9798400723094},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3774906.3802761},
doi = {10.1145/3774906.3802761},
abstract = {Occupational exposure to airborne particulate matter (PM) poses a severe health risk in open industrial workspaces such as stone-cutting 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.},
booktitle = {Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems},
pages = {305–318},
numpages = {14},
keywords = {mmWave Sensing, View Adaptation, Re-identification, Pollution Exposure},
location = {
},
series = {SenSys '26}
}