  Hand gesture is one of the most important means of touchless communication
between human and machines. There is a great interest for commanding electronic
equipment in surgery rooms by hand gesture for reducing the time of surgery and
the potential for infection. There are challenges in implementation of a hand
gesture recognition system. It has to fulfill requirements such as high
accuracy and fast response. In this paper we introduce a system of hand gesture
recognition based on a deep learning approach. Deep learning is known as an
accurate detection model, but its high complexity prevents it from being
fabricated as an embedded system. To cope with this problem, we applied some
changes in the structure of our work to achieve low complexity. As a result,
the proposed method could be implemented on a naive embedded system. Our
experiments show that the proposed system results in higher accuracy while
having less complexity in comparison with the existing comparable methods.
