Position and Orientation Agnostic Gesture Recognition Using WiFi

WiFi based gesture recognition systems have recently proliferated due to the ubiquitous availability of WiFi in almost every modern building. The key limitation of existing WiFi based gesture recognition systems is that they require the user to be in the same configuration (i.e., at the same position and in same orientation) when performing gestures at runtime as when providing training samples, which significantly restricts their practical usability. In this paper, we propose a WiFi based gesture recognition system, namely WiAG, which recognizes the gestures of the user irrespective of his/her configuration. The key idea behind WiAG is that it first requests the user to provide training samples for all gestures in only one configuration and then automatically generates virtual samples for all gestures in all possible configurations by applying our novel translation function on the training samples. Next, for each configuration, it generates a classification model using virtual samples corresponding to that configuration. To recognize gestures of a user at runtime, as soon as the user performs a gesture, WiAG first automatically estimates the configuration of the user and then evaluates the gesture against the classification model corresponding to that estimated configuration. Our evaluation results show that when user's configuration is not the same at runtime as at the time of providing training samples, WiAG significantly improves the gesture recognition accuracy from just 51.4% to 91.4%.