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.

翻译：人体运动分析因其在机器人交互、人类学习应用及临床诊断等领域中的广泛应用而受到深入研究。然而，当前最先进方法在人体运动建模时仍面临科学挑战。首先，新模型必须考虑人体运动的随机性与人体物理结构，以准确预测全身运动描述符随时间演化的规律。其次，尽管采用深度学习算法，其体态预测方面的可解释性仍需提升——现有模型缺乏对人体运动的可理解表征。本文通过提出三种创新方法来解决这些挑战，旨在构建可解释的人体运动表征。本研究将人体运动建模为符合手势操作模型（GOM）结构的状态空间模型，其参数通过深度学习与统计算法进行估计。训练后的模型既可用于识别专家从业者全身关节间的动态关联以开展灵巧性分析，亦可生成人工专业运动数据。