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Mohamed Haseeb

Software and machine learning engineer, interested in applying machine learning techniques to build innovative solutions.

Wisture: Touch-less Hand Gesture Classification in Unmodified Smartphones Using Wi-Fi Signals


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.