WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
This addresses the problem of limited practicability in multi-user scenarios for researchers in WiFi-based human sensing, though it is incremental as it focuses on dataset creation rather than a new method.
The paper tackles the lack of benchmark datasets for WiFi-based multi-user activity sensing by introducing WiMANS, the first such dataset, which contains over 9.4 hours of dual-band WiFi CSI and synchronized videos, and benchmarks state-of-the-art models to reveal new challenges and opportunities.
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.