WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. Although recent studies have begun to investigate WiFi-based multi-user sensing, there remains a lack of benchmark datasets to facilitate reproducible and comparable research. To bridge this gap, we present WiMANS, to our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as well as synchronized videos, monitoring simultaneous activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing.
@article{arxiv.2402.09430,
title = {WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing},
author = {Shuokang Huang and Kaihan Li and Di You and Yichong Chen and Arvin Lin and Siying Liu and Xiaohui Li and Julie A. McCann},
journal= {arXiv preprint arXiv:2402.09430},
year = {2024}
}
Comments
We present WiMANS, to our knowledge, the first dataset for multi-user activity sensing based on WiFi