Wi-Fi Sensing for Human Activity Recognition: A Monostatic Approach via Beamforming Feedback Matrix

Wi-Fi\u2013based Human Activity Recognition (HAR) faces significant deployment barriers because most existing systems rely on Channel State Information (CSI), which requires multiple transceiver devices, including specific hardware, driver access, or firmware modification. Recent studies have explored Beamforming Feedback Matrix (BFM) signals as a lightweight alternative; however, these approaches typically employ bistatic configurations with physically separated transmitters and receivers, limiting practicality in residential deployments. This paper presents a firmware-agnostic, monostatic HAR framework that leverages BFM signals captured by a single commercial IEEE 802.11ac router, eliminating the need for auxiliary transceivers or hardware modification. A principled signal processing and feature engineering pipeline is introduced, incorporating Signal-to-Noise Ratio (SNR) filtering and BFM\u2013acceleration extraction to mitigate noise and nonstationarity in compressed beamforming data. The proposed system is evaluated using 150 experimental sessions, involving five subjects across three distinct environments, under a LeaveOne-Subject-Out (LOSO) cross-validation protocol. Experimental results demonstrate classification accuracy of up to 98.75% for unseen subjects within the same environment and over 93% accuracy under cross-environment testing. These results validate that monostatic BFM sensing retains sufficient spatial and temporal structure for robust activity recognition, establishing a practical pathway toward scalable, firmware-agnostic Wi-Fi sensing using commodity devices.