2024/01/24 by Shuokang Huang, Huang, Shuokang, Kaihan Li +13 · 7 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Human Mobility and Location-Based Analysis #Multimedia (cs.MM) #Signal Processing (eess.SP) #Wireless Networks and Protocols #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.09430
openalex publication_date 2024/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.