WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
Shuokang Huang*, Kaihan Li, Di You, Yichong Chen, Arvin Lin, Siying Liu, Xiaohui Li, Julie A. McCann*
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 the 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."
BibTeX
@inproceedings{eccv2024_wimansabenchmark,
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*},
booktitle = {ECCV 2024},
year = {2024}
}