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

The analysis of human movements has been extensively studied due to its wide variety of practical applications. Nevertheless, the state-of-the-art still faces scientific challenges while modeling human movements. Firstly, new models that account for the stochasticity of human movement and the physical structure of the human body are required to accurately predict the evolution of full-body motion descriptors over time. Secondly, the explainability of existing deep learning algorithms regarding their body posture predictions while generating human movements still needs to be improved as they lack comprehensible representations of human movement. This paper addresses these challenges by introducing three novel approaches for creating explainable representations of human movement. In this work, full-body movement is formulated as a state-space model of a dynamic system whose parameters are estimated using deep learning and statistical algorithms. The representations adhere to the structure of the Gesture Operational Model (GOM), which describes movement through its spatial and temporal assumptions. Two approaches correspond to deep state-space models that apply nonlinear network parameterization to provide interpretable posture predictions. The third method trains GOM representations using one-shot training with Kalman Filters. This training strategy enables users to model single movements and estimate their mathematical representation using procedures that require less computational power than deep learning algorithms. Ultimately, two applications of the generated representations are presented. The first is for the accurate generation of human movements, and the second is for body dexterity analysis of professional movements, where dynamic associations between body joints and meaningful motion descriptors are identified.