  Due to the mass advancement in ubiquitous technologies nowadays, new
pervasive methods have come into the practice to provide new innovative
features and stimulate the research on new human-computer interactions. This
paper presents a hand gesture recognition method that utilizes the smartphone's
built-in speakers and microphones. The proposed system emits an ultrasonic
sonar-based signal (inaudible sound) from the smartphone's stereo speakers,
which is then received by the smartphone's microphone and processed via a
Convolutional Neural Network (CNN) for Hand Gesture Recognition. Data
augmentation techniques are proposed to improve the detection accuracy and
three dual-channel input fusion methods are compared. The first method merges
the dual-channel audio as a single input spectrogram image. The second method
adopts early fusion by concatenating the dual-channel spectrograms. The third
method adopts late fusion by having two convectional input branches processing
each of the dual-channel spectrograms and then the outputs are merged by the
last layers. Our experimental results demonstrate a promising detection
accuracy for the six gestures presented in our publicly available dataset with
an accuracy of 93.58\% as a baseline.
