  Efficient and quick remote communication in search and rescue operations can
be life-saving for the first responders. However, while operating on the field
means of communication based on text, image and audio are not suitable for
several disaster scenarios. In this paper, we present a smartwatch-based
application, which utilizes a Deep Learning (DL) model, to recognize a set of
predefined arm gestures, maps them into Morse code via vibrations enabling
remote communication amongst first responders. The model performance was
evaluated by training it using 4,200 gestures performed by 7 subjects
(cross-validation) wearing a smartwatch on their dominant arm. Our DL model
relies on convolutional pooling and surpasses the performance of existing DL
approaches and common machine learning classifiers, obtaining gesture
recognition accuracy above 95%. We conclude by discussing the results and
providing future directions.
