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

Semantic Labeling of Human Action For Visually Impaired And Blind People Scene Interaction