CBR-HAR: Real-Time Human Activity Recognition Based on CBR

Fangming Tian, Yufei Jiang, Chenyu Bao, Baicheng Peng, Feng Hong, Zhongwen Guo

Abstract: WiFi-based human activity recognition (HAR) plays a pivotal role in applications such as elderly care, health monitoring, and smart home systems. Unlike the traditional IEEE 802.11n that rely on full channel state information (CSI), modern WiFi standards, including IEEE 802.11ac/ax, use compressed beamforming reports (CBR) instead of exchanging CSI between commercial off-the-shelf (COTS) routers and wireless devices. This partial CSI poses significant challenges for activity recognition. In this letter, we introduce CBR-HAR, a real-time activity recognition system leveraging WiFi signals compliant with the IEEE 802.11ac/ax standards. CBR-HAR consists of a CBR capture and decoding module, a time-domain and frequency-domain feature extraction module, and an activity recognition module. In the activity recognition module, we propose a dual-branch residual network to effectively utilize both the time-domain and frequency-domain information of the CBR to classify human activities. Additionally, to better distinguish between falls and similar activities such as standing up and sitting down, we integrate a BERT-based semantic embedding loss. Extensive evaluations show that CBR-HAR achieves an impressive accuracy of 90.56% in classifying six kinds of human activities.

External IDs:dblp:journals/wcl/TianJBPHG25