Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation

Abstract

Extensive research has been conducted on analyzing human movements, driven by its diverse practical applications such as human–robot interaction, human learning, and clinical diagnosis. However, the current state-of-the-art still encounters scientific challenges when it comes to modeling human movements. There are two key aspects that need to be addressed. Firstly, new models should consider the stochastic nature of human movement and the physical structure of the human body to accurately predict the patterns in full-body motion descriptors over time. Secondly, while deep learning algorithms have been utilized, they lack explainability in terms of predicting body posture sequences, making it essential to improve their comprehensible representation of human movement. This paper aims to tackle these challenges by presenting three innovative methods for creating explainable representations of human movement. The study formulates human body movement as a state-space model based on the Gesture Operational Model (GOM). Model parameters are estimated through either one-shot training employing Kalman Filters or data-intensive training utilizing artificial neural networks. The trained models are utilized for analyzing the dexterity of expert professionals in full-body movements, enabling the identification of dynamic associations between body joints and gesture recognition. Additionally, these models are employed to generate artificial professional movements.