  Gesture is an important mean of non-verbal communication, with visual
modality allows human to convey information during interaction, facilitating
peoples and human-machine interactions. However, it is considered difficult to
automatically recognise gestures. In this work, we explore three different
means to recognise hand signs using deep learning: supervised learning based
methods, self-supervised methods and visualisation based techniques applied to
3D moving skeleton data. Self-supervised learning used to train fully
connected, CNN and LSTM method. Then, reconstruction method is applied to
unlabelled data in simulated settings using CNN as a backbone where we use the
learnt features to perform the prediction in the remaining labelled data.
Lastly, Grad-CAM is applied to discover the focus of the models. Our
experiments results show that supervised learning method is capable to
recognise gesture accurately, with self-supervised learning increasing the
accuracy in simulated settings. Finally, Grad-CAM visualisation shows that
indeed the models focus on relevant skeleton joints on the associated gesture.
