Demo: Real-time mmWave Radar Human Sensing Testbed
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.
Authors
- Ruofeng Liu (ORCID: https://orcid.org/0000-0001-9804-3727)
- Wenchao Jiang (ORCID: https://orcid.org/0000-0001-6765-2466)
- Ruixia Shi (ORCID: https://orcid.org/0009-0008-0637-195X)
- Shuai Wang (ORCID: https://orcid.org/0000-0003-2766-1135)
- Luoyu Mei (ORCID: https://orcid.org/0000-0003-2338-0256)
- Taiwei Ling (ORCID: https://orcid.org/0009-0001-6555-5176)
- Shuai Wang (ORCID: https://orcid.org/0000-0002-3609-2205)
- Weiwei Chen (ORCID: https://orcid.org/0000-0003-4991-4076)
Institutions
- Shanghai University (CN)
- Singapore University of Technology and Design (SG)
- Southeast University (CN)
- Michigan State University (US)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1145/3636534.3698860
- Citations
- 2
- Primary Topic
- Indoor and Outdoor Localization Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.42