Deep state-space modeling for explainable representation, analysis, and generation of professional human poses
This work addresses the problem of explainable human movement modeling for applications like human-robot interaction and clinical diagnosis, but it appears incremental as it builds on existing frameworks like the Gesture Operational Model.
The paper tackles the challenges of modeling stochastic human movement and improving explainability in deep learning by introducing three novel methods for creating explainable representations of human movement, resulting in models used for full-body dexterity analysis of experts and generating artificial professional movements.
The analysis of human movements has been extensively studied due to its wide variety of practical applications, such as human-robot interaction, human learning applications, or clinical diagnosis. Nevertheless, the state-of-the-art still faces scientific challenges when modeling human movements. To begin, new models must account for the stochasticity of human movement and the physical structure of the human body in order to accurately predict the evolution of full-body motion descriptors over time. Second, while utilizing deep learning algorithms, their explainability in terms of body posture predictions needs to be improved as they lack comprehensible representations of human movement. This paper addresses these challenges by introducing three novel methods for creating explainable representations of human movement. In this study, human body movement is formulated as a state-space model adhering to the structure of the Gesture Operational Model (GOM), whose parameters are estimated through the application of deep learning and statistical algorithms. The trained models are used for the full-body dexterity analysis of expert professionals, in which dynamic associations between body joints are identified, and for generating artificially professional movements.