  The lack of adequate training data is one of the major hurdles in WiFi-based
activity recognition systems. In this paper, we propose Wi-Fringe, which is a
WiFi CSI-based device-free human gesture recognition system that recognizes
named gestures, i.e., activities and gestures that have a semantically
meaningful name in English language, as opposed to arbitrary free-form
gestures. Given a list of activities (only their names in English text), along
with zero or more training examples (WiFi CSI values) per activity, Wi-Fringe
is able to detect all activities at runtime. In other words, a subset of
activities that Wi-Fringe detects do not require any training examples at all.
