Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition
This addresses data scarcity for WiFi-based gesture recognition systems, though it appears incremental by focusing on named gestures rather than arbitrary ones.
The paper tackles the problem of limited training data in WiFi-based activity recognition by introducing Wi-Fringe, a system that recognizes named gestures using WiFi CSI, achieving detection of activities with zero or few training examples.
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.