CBR-ACE: Counting Human Exercise using Wi-Fi Beamforming Reports

Abstract

As people spend more time indoors owing to the COVID-19 global pandemic, the automatic detection of indoor human activity has increasingly become of interest to researchers and consumers. Conventional Wi-Fi Channel State Information (CSI)-based detection provides adequate accuracy; however, they have a deployment constraint owing to specific hardware and software for full CSI acquisition. This study exploits the Compressed Beamforming Report (CBR), which is a default form of CSI in IEEE 802.11ac and 11ax, to address the constraint in Wi-Fi CSIbased methods. The CBRs are shared among most IEEE 802.11ac compliant devices and are easily obtained with outer sniffers. Our CBR-based Activity Count Estimator (CBR-ACE) is a novel wireless sensing system using CBRs. The CBR-ACE provides a Raspberry Pi-based tool to easily deploy a new wireless sensing system into existing networks, and utilizes the CBR irregularity for automatic detection. From experiments in real-dwelling environments, the proposed CBR-ACE achieves average estimation errors of 0.97 in the best case.