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This has been in development worldwide for quite a while. I've got a neighbor who was key in the early Wi-Fi systems development and standardization. He knows all about progress in this sensing area.

The Wi-Fi working group developing the use of Wi-Fi to track human bodies on premises is the IEEE 802.11bf Task Group (TGbf), established under the IEEE 802.11 standards body. This group is focused on standardizing Wi-Fi Sensing, which leverages Wi-Fi signals to detect and track human presence, movement, and activities through techniques like analyzing Channel State Information (CSI). The IEEE 802.11bf specification, approved in September 2020, aims to formalize Wi-Fi sensing capabilities, enabling applications such as motion detection, gesture recognition, and biometric identification using existing Wi-Fi infrastructure. The Wi-Fi Alliance, a non-profit organization, also supports this effort by promoting interoperability and certification for Wi-Fi sensing technologies.

Progress...

Wi-Fi Sensing enables diverse use cases, including smart home automation, security, health monitoring, and gesture-based control, reducing the need for dedicated sensors like radars.
The standards group released Draft 0.1 in April 2022, Draft 1.0 in July 2022, Draft 2.0 in January 2023, and Draft 3.0 by December 2023. The final standard, IEEE Std 802.11bf™-2025, was published in 2025, with availability expected soon.
The amendment supports bistatic and multistatic sensing, using Channel State Information for applications like presence detection, gesture recognition, health monitoring (e.g., fall detection, heart rate monitoring), and in-car sensing. It standardizes sensing measurement exchanges during configurable sensing windows, ensuring minimal impact on data communication.
Techniques include time-of-flight (ToF), Doppler shift, and angle-of-arrival (AoA) estimation, with 60 GHz bands enabling high-resolution sensing due to shorter wavelengths. AI and machine learning (ML) algorithms, such as deep neural networks and convolutional neural networks, are increasingly used to filter noise, classify motion patterns, and improve sensing accuracy in complex environments. Research focuses on cross-band fusion (combining sub-7 GHz and 60 GHz data) for enhanced robustness.
R&D has prioritized backward compatibility with existing Wi-Fi standards (Wi-Fi 4/5/6/7), allowing sensing on off-the-shelf devices without hardware changes. Protocols minimize sensing overhead to avoid degrading data communication performance.
Experimental systems demonstrate Wi-Fi sensing for fall detection, sleep monitoring, intruder detection, and touchless interfaces. For example, prototypes achieve sub-meter accuracy for indoor localization and can detect vital signs through walls using advanced signal processing.
While the 802.11bf standard is complete, R&D continues to optimize algorithms, reduce latency, and expand use cases, with commercial deployments expected to grow as certified devices adopt the standard. Integration with edge computing and IoT ecosystems is a focus, enabling real-time processing for smart homes, healthcare, and security. Research also explores privacy-preserving sensing to address concerns about unauthorized monitoring.
Widespread commercial adoption on off-the-shelf devices is still in progress, with ongoing efforts to refine algorithms and ensure seamless integration.