Wisture: Touch-less Hand Gesture Classification in Unmodified Smartphones Using Wi-Fi Signals01 Nov 2018 | time series LSTM pattern recognition Android
Our paper (together with Dr. Ramviyas Parasuraman) got accepted for publication in IEEE Sensors Journal (2018). Subset of the work reported was part of my master’s thesis. A preprint can be found here, and the paper can be found here.
The paper introduces Wisture, a solution for recognizing touch-less dynamic hand gestures on smartphones from the Wi-Fi Received Signal Strength (RSS). Unlike other Wi-Fi based gesture recognition methods, the proposed method does not require a modifying the smartphone hardware or the operating system, and performs the gesture recognition without interfering with the normal operation of other smartphone applications. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is trained to predict hand gestures from a pre-processed Wi-Fi RSS input sequence.
Below is a video demonstration of Wisture.