Demo: Real-time mmWave Radar Human Sensing Testbed

Ruofeng Liu, Shuai Wang, Wenchao Jiang, Weiwei Chen, Ruili Shi, Luoyu Mei, Taiwei Lin

Abstract: Millimeter-wave (mmWave) radar is emerging as a promising sensor for various human sensing tasks. Deep learning is frequently applied in radar-based applications, which typically require extensive data collection and labeling. In this demo, we present a low-cost hardware setup and a cross-platform software pipeline that automatically captures radar data of human activities, labels ground truth, and tests inference models in real time. The effectiveness of the testbed is demonstrated through real-time human pose estimation.

External IDs:doi:10.1145/3636534.3698860