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WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing (2402.09430v2)

Published 24 Jan 2024 in eess.SP, cs.AI, cs.CV, and cs.MM

Abstract: 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.

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Authors (8)
  1. Shuokang Huang (2 papers)
  2. Kaihan Li (2 papers)
  3. Di You (13 papers)
  4. Yichong Chen (1 paper)
  5. Arvin Lin (1 paper)
  6. Siying Liu (9 papers)
  7. Xiaohui Li (26 papers)
  8. Julie A. McCann (22 papers)
Citations (1)
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