  The aim of this work is to contribute to the development of a tactile device
for visually impaired and blind persons in order to let them to understand
actions of the surrounding people and to interact with them. First, based on
the state-of-the-art methods of human action recognition from RGB-D sequences,
we use the skeleton information provided by Kinect, with the disentangled and
unified multi-scale Graph Convolutional (MS-G3D) model to recognize the
performed actions. We tested this model on real scenes and found some of
constraints and limitations. Next, we apply a fusion between skeleton modality
with MS-G3D and depth modality with CNN in order to bypass the discussed
limitations. Third, the recognized actions are labeled semantically and will be
mapped into an output device perceivable by the touch sense.
