collaborators

6 papers

eess.SP2026

Lightweight Range-Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar

Huy Trinh, George Shaker

Quasi-static human activities such as lying, standing or sitting produce very low Doppler shifts and highly spread radar signatures, making them difficult to detect with convention…

eess.SP2026

Generative Latent Alignment for Interpretable Radar Based Occupancy Detection in Ambient Assisted Living

Huy Trinh

In this work, we study how to make mmWave radar presence detection more interpretable for Ambient Assisted Living (AAL) settings, where camera-based sensing raises privacy concerns…

eess.SP2026

A Physics-Informed Digital Twin Framework for Calibrated Sim-to-Real FMCW Radar Occupancy Estimation

Huy Trinh, Sebastian Ratto, Elliot Creager +1

Learning robust radar perception models directly from real measurements is costly due to the need for controlled experiments, repeated calibration, and extensive annotation. This p…

eess.SP2026

Doppler-Domain Respiratory Amplification for Semi-Static Human Occupancy Detection Using Low-Resolution SIMO FMCW Radar

Huy Trinh, Elliot Creager, George Shaker

Radar-based sensing is a promising privacy-preserving alternative to cameras and wearables in settings such as long-term care. Yet detecting quasi-static presence (lying, sitting,…

eess.SP2026

Reliable Quasi-Static Post-Fall Floor-Occupancy Detection Using Low-Cost Millimetre-Wave Radar

Huy Trinh, Phuong Thai, Elliot Creager +1

As the population ages rapidly, long-term care (LTC) facilities across North America face growing pressure to monitor residents safely while keeping staff workload manageable. Fall…

eess.SP2026

Radar-Based Fall Detection for Assisted Living: A Digital-Twin Representation Case Study

Sebastian Ratto, Huy Trinh, Ahmed N. Sayed +3

Obtaining data on high-impact falls from older adults is ethically difficult, yet these rare events cause many fall-related health problems. As a result, most radar-based fall dete…