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The MOBOT project has envisioned the development of cognitive robotic assistant prototypes that act proactively, adaptively and interactively with respect to elderly humans with slight walking and cognitive difficulties. To meet the project's goals, a multimodal action recognition system is being developed to monitor, analyse and predict user actions with a high level of accuracy and detail. Here we discuss how the analysis of human behaviour data that have become available through the annotation and study of the project's multimodal-multisensory corpus, have led to the modelling of Human-Robot Communication in order to achieve an effective, natural interaction between users and the assistive robotic platform, intending to show how the project's communication model has been integrated in the robotic platform in order to support a natural multimodal human-robot interaction as verified by systematic end user validation cycles. |
