  Hand gesture recognition constitutes the initial step in most methods related
to human-robot interaction. There are two key challenges in this task. The
first one corresponds to the difficulty of achieving stable and accurate hand
landmark predictions in real-world scenarios, while the second to the decreased
time of forward inference. In this paper, we propose a fast and accurate
framework for hand pose estimation, dubbed as "FastHand". Using a lightweight
encoder-decoder network architecture, we achieve to fulfil the requirements of
practical applications running on embedded devices. The encoder consists of
deep layers with a small number of parameters, while the decoder makes use of
spatial location information to obtain more accurate results. The evaluation
took place on two publicly available datasets demonstrating the improved
performance of the proposed pipeline compared to other state-of-the-art
approaches. FastHand offers high accuracy scores while reaching a speed of 25
frames per second on an NVIDIA Jetson TX2 graphics processing unit.
