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

arXiv: https://arxiv.org/abs/2505.21866 · HTML: https://mlsanity.com/paper/2505.21866/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points](https://mlsanity.com/paper/2103.09924/) (2021)
- [Joint Activity Recognition and Indoor Localization with WiFi Fingerprints](https://mlsanity.com/paper/1904.04964/) (2019)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [Wi-Fi Sensing: Applications and Challenges](https://mlsanity.com/paper/1901.00715/) (2019)
- [Robust Proximity Detection using On-Device Gait Monitoring](https://mlsanity.com/paper/2404.19182/) (2024)

## Built on by (most cited first, 9 of 9)

- [AM-FM: A Foundation Model for Ambient Intelligence Through WiFi](https://mlsanity.com/paper/2602.11200/) (2026)
- [CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision](https://mlsanity.com/paper/2605.14171/) (2026)
- [Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT](https://mlsanity.com/paper/2512.15206/) (2025)
- [Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness](https://mlsanity.com/paper/2511.20456/) (2025)
- [FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing](https://mlsanity.com/paper/2512.22143/) (2025)
- [FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing](https://mlsanity.com/paper/2608.08439/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [CSI-Agent: LLM-Assisted Few-Shot Adaptation for Cross-Domain Wi-Fi CSI Sensing](https://mlsanity.com/paper/2609.31990/) (2026)
- [UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing](https://mlsanity.com/paper/2610.09559/) (2026)

## Abstract (arXiv)

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.