WiGest: A Ubiquitous WiFi-based Gesture Recognition System

TL;DR

WiGest is a WiFi-based gesture recognition system that detects in-air hand gestures using standard WiFi equipment without modifications or training, achieving high accuracy in various environments.

Contribution

WiGest introduces a novel WiFi-based gesture recognition method that requires no hardware modifications or training, utilizing signal primitives to identify gestures.

Findings

Achieves 87.5% accuracy with a single AP in non-line-of-sight scenarios

Improves to 96% accuracy using three APs

Successfully classifies gestures in real-world multimedia applications

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. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modi-fications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes 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 laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy in-creases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. 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.