AI uses Wi-Fi data to estimate how many people are in a room

You can tell a lot about people from their Wi-Fi connections -- including, as it turns out, how many of them are standing near an access point. In a newly published research paper ("DeepCount: Crowd Counting with WiFi via Deep Learning") on the preprint server Arxiv.org, Their work comes not long after researchers at Ryerson University in Toronto demonstrated a neural network that can determine whether smartphone owners are walking, biking, or driving around a few city blocks by using Wi-Fi data, and after Purdue University researchers developed a system that uses Wi-Fi access logs to suss out relationships among users, locations, and activities. In this latest study, the team leveraged channel state information (CSI) -- specifically phase and amplitude -- to create a two-model system consisting of an activity recognition model and deep learning model. The deep learning model was tasked with correlating the number of people and channels by mapping those people's activities to CSI, while the former recognized when someone entered or left the room via an electronic switch.