CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing

• URL: http://arxiv.org/abs/2505.21866v1
• Date: Wed, 28 May 2025 01:29:29 GMT
• Title: CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing
• Authors: Guozhen Zhu, Yuqian Hu, Weihang Gao, Wei-Hsiang Wang, Beibei Wang, K. J. Ray Liu,
• Abstract summary: WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI)<n>Existing WiFi sensing systems struggle to generalize in real-world settings due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect daily activity.<n>We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users.
• Score: 13.709208651007167
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to generalize in real-world settings, largely due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect continuous daily activity. We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users. Spanning over 461 hours of effective data, CSI-Bench captures realistic signal variability under natural conditions. It includes task-specific datasets for fall detection, breathing monitoring, localization, and motion source recognition, as well as a co-labeled multitask dataset with joint annotations for user identity, activity, and proximity. To support the development of robust and generalizable models, CSI-Bench provides standardized evaluation splits and baseline results for both single-task and multi-task learning. CSI-Bench offers a foundation for scalable, privacy-preserving WiFi sensing systems in health and broader human-centric applications.

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