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

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