Wi-Fi Gesture Recognition on Existing Devices
This addresses the need for convenient, device-free gesture recognition in everyday environments, representing a novel application rather than an incremental improvement.
The paper tackled the problem of enabling gesture recognition using existing Wi-Fi signals and devices, achieving a classification accuracy of 91% for four gestures across six participants without per-participant training.
This paper introduces the first wireless gesture recognition system that operates using existingWi-Fi signals and devices. To achieve this, we first identify limitations of existing wireless gesture recognition approaches that limit their applicability to Wi-Fi. We then introduce algorithms that can classify gestures using information that is readily available on Wi-Fi devices. We demonstrate the feasibility of our design using a prototype implementation on off-the-shelf Wi-Fi devices. Our results show that we can achieve a classification accuracy of 91% while classifying four gestures across six participants, without the need for per-participant training. Finally, we show the feasibility of gesture recognition in non-line-ofsight situations with the participants interacting with a Wi-Fi device placed in a backpack.