A Ubiquitous WiFi-Based Fine-Grained Gesture Recognition System

Abstract

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. WiGest uses standard WiFi equipment, with no modifications, and requires no training for gesture recognition. The system identifies different RSS change primitives, from which we construct mutually-independent gesture families. These families can be mapped to distinguishable application actions. More fine-grained features can also be recognized for the detected primitives using CSI. WiGest addresses various challenges including cleaning the noisy signals, gesture type and attribute detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf devices and extensively evaluate the system in two different environments. Our results show that WiGest detects the basic primitives with an accuracy of 87.5 percent using one AP, including through-the-wall non-line-of-sight scenarios, which increases to 96 percent using three overheard APs. Additionally, when evaluating the system using a multi-media player application, we achieve an accuracy of 96 percent. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.