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The analysis of human movements has been extensively studied due to its widevariety of practical applications, such as human-robot interaction, humanlearning applications, or clinical diagnosis. Nevertheless, thestate-of-the-art still faces scientific challenges when modeling humanmovements. To begin, new models must account for the stochasticity of humanmovement and the physical structure of the human body in order to accuratelypredict the evolution of full-body motion descriptors over time. Second, whileutilizing deep learning algorithms, their explainability in terms of bodyposture predictions needs to be improved as they lack comprehensiblerepresentations of human movement. This paper addresses these challenges byintroducing three novel methods for creating explainable representations ofhuman movement. In this study, human body movement is formulated as astate-space model adhering to the structure of the Gesture Operational Model(GOM), whose parameters are estimated through the application of deep learningand statistical algorithms. The trained models are used for the full-bodydexterity analysis of expert professionals, in which dynamic associationsbetween body joints are identified, and for generating artificiallyprofessional movements.

Deep state-space modeling for explainable representation, analysis, and generation of professional human poses