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CSI Enabled Literature & Projects

The transition of Wi-Fi sensing from a theoretical concept to a deployable engineering solution is well documented across recent academic literature and open-source implementations. The following sections break down a few papers that proved edge-based sensing is viable, alongside some datasets and tools driving that are pupular in the space.

Existing Literature

Many academic papers look into and implement Wi-Fi sensing techniques via CSI to achieve goals such as HAR, Wi-Fi positioning & localization, crowd counting, sleep analysis etc. A few notable papers that look specifically into edge Wi-Fi sensing are:

1. WiFi Sensing on the Edge: Signal Processing Techniques and Challenges (Hernandez & Bulut, 2022) 1

This paper evaluates the feasibility of migrating CSI signal processing pipelines, historically reliant on powerful computers directly onto constrained microcontrollers (specifically the ESP32).

  • Proofs of Concept Achieved:

    • Validated that ESP32 microcontrollers can handle local CSI sampling, filtering, and machine learning inference without cloud offloading.

    • Demonstrated three tiers of sensing: small-scale hand gesture recognition (Human-Computer Interaction), medium-scale human activity recognition (HAR), and large-scale localization.

  • Lessons Learned:

    • Raw CSI processing is computationally expensive, but quantization techniques can be employed to fit models onto edge devices.

    • Standalone IoT devices drastically reduce the cost and physical footprint of sensing arrays compared to legacy Linux-based setups.

  • Remaining Gaps:

    • Environment-Specific Calibration: Most models fail when moved to a new room. The paper highlights the urgent need for online, real-time calibration methods rather than offline, lab-based training.

    • Scalability is still hindered by the data-hungry nature of the models, requiring massive data collection at every new deployment site.

2. Tools and Methods for Achieving Wi-Fi Sensing in Embedded Devices (Armenta-Garcia et al., 2025) 2

This paper directly attacks the hardware and memory constraints of edge AI, proposing an end-to-end CSI collection and inference framework explicitly built for the ESP32-S3.

  • Proofs of Concept Achieved:

    • Developed a DenseNet-based HAR model that operates entirely on-device, achieving 92.43% accuracy with an inference latency of just 232 ms.

    • Shrank the operational memory footprint of the deep learning model to 127 kB.

    • Solved the scarcity of training data using an Empirical Mode Decomposition (EMD)-based data augmentation strategy, bumping model accuracy from an initial 59.91% to over 97%.

  • Lessons Learned:

    • True privacy-preserving HAR is possible. Because inference happens entirely at the edge, no raw RF data or personal activity data needs to be transmitted to the cloud.

    • Hardware specifics matter: Efficient packet rate handling under standard baud rates is critical to prevent pipeline bottlenecks on microcontrollers.

  • Remaining Gaps:

    • Non-Stationary Conditions: Robust signal preprocessing under changing environmental conditions (furniture moving, temperature changes) remains unsolved for long-term stability.

    • Multi-Antenna Synchronization: To improve spatial resolution, multi-antenna arrays are needed, but synchronizing them accurately on embedded RTOS hardware without dedicated clock lines is highly difficult.

CSI Enabled Projects & Open Ecosystems

The open-source community has made good use of CSI enabled sensing, a few examples borrowed from the Awesome-WiFi-CSI-Sensing3 are:

Frameworks and Visualization Tools

  • SenseFi: An extensive library and benchmarking suite for evaluating deep learning models specifically designed for Wi-Fi human sensing. It provides the Python boilerplate for testing LSTMs, CNNs, and Autoencoders on raw CSI arrays.

  • ESP32-CSI-Tool: Developed alongside Hernandez’s research, this C-based tool provides the foundational scripts for extracting CSI payloads from Espressif hardware and piping them via serial or WebSockets for visualization. 4

Open Datasets

  • MM-Fi: A large-scale multimodal dataset that aligns Wi-Fi CSI with RGB-D cameras, LiDAR, and mmWave Radar. It covers 40 human subjects and over 20 action categories, useful for cross-modal training.

  • Widar 3.0: Focused strictly on gesture recognition, containing 258,000 instances of hand gestures collected across 75 different physical domains.

  • NTU-Fi: A high-resolution dataset containing 114 subcarriers per antenna pair, tracking 6 human activities and 14 specific gait patterns.

Advanced Applications

  • Vital Sign Detection: Extracting micro-movements (chest displacement from respiration and heartbeats) from phase shifts.

  • Pose Estimation (DensePose_from_WiFi): Using deep learning to map CSI scattering directly to human skeletal wireframes, effectively giving Wi-Fi “x-ray” vision through walls.

  • LLM Integration (HoloLLM / X-Fi): The latest edge research is feeding processed CSI tokens directly into multimodal Large Language Models to allow systems to perform semantic reasoning based on physical Wi-Fi disturbances.

Summary

This Chapter was a short one, but to summarize, we looked at:

  • How this is still a very much new field, with native support at the hardware level only emerging in the last few years.
  • How the research community has been quick to employ CSI sensing in all sorts of applications.
  • What cool projects and datasets are out there for people to use and build on.

  1. Hernandez, S. M., & Bulut, E. (2022). WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems. IEEE Communications Surveys & Tutorials. https://ieeexplore.ieee.org/document/9900419 ↩

  2. Armenta-Garcia, J.A., Gonzalez-Navarro, F.F., Caro-Gutierrez, J., & Garcia-Reyes, C.I. (2025). Tools and Methods for Achieving Wi-Fi Sensing in Embedded Devices. Sensors, 25(19), 6220. https://doi.org/10.3390/s25196220 ↩

  3. NTU MARS Lab. (2025). Awesome WiFi Sensing. GitHub Repository. https://github.com/NTUMARS/Awesome-WiFi-CSI-Sensing ↩

  4. Hernandez, S. M. (2020). ESP32-CSI-Tool. GitHub Repository. https://stevenmhernandez.github.io/ESP32-CSI-Tool/ ↩