Environmental Microchanges in WiFi Sensing

Using WiFi's Channel State Information for human activity recognition—referred to as WiFi sensing—has attracted considerable attention. But despite this interest and many publications over a decade, WiFi sensing has not yet found its way into practice because of a lack of robustness of the inference results. In this paper, we quantitatively show that even “microchanges” in the environment can significantly impact WiFi signals, and potentially alter the ML inference results. We therefore argue that new training and inference techniques might be necessary for mainstream adoption of WiFi sensing.