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 activity 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 activity sensing
based on WiFi. WiMANS contains over 9.4 hours of WiFi Channel State Information
(CSI), monitoring simultaneous activities performed by multiple users in
various environments. Compared to existing datasets, WiMANS not only collects
the CSI of dual WiFi bands but also includes synchronized videos. 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 WiFi-based multi-user identification, localization, and activity
recognition. We believe that WiMANS can push the boundaries of current
WiFi-based human sensing and catalyze the research on multi-user activity
analysis.

基于 WiFi 的多人活动感知的首个数据集 WiMANS 在多个环境中监测了多个用户的同时活动，并包括双 WiFi 频段的信道状态信息以及视频，用于评估 WiFi 和基于视频的人类感知模型的性能，为多用户识别、本地化和活动识别提供新的挑战和机会。