Artificial intelligence (AI) is a scientific and engineering discipline that involves designing systems capable of autonomously replicating the cognitive functions typically associated with human intelligence. Current AI uses data to extract patterns, supports decision-making, and enhances analytical reasoning across diverse domains, including sports performance or strategic claims, and assists in clinical applications. In sports, AI enables robotic systems to assist in training, object tracking, performance monitoring, strategy development, and talent identification. In medicine and rehabilitation, AI facilitates robotic surgery, rehabilitation training, and decision-support systems. Machine learning and deep learning techniques, combined with computer vision, enable estimation of human posture and movement in 2D or 3D from video recordings, providing objective, quantitative, and markerless movement analysis. For instance, human pose estimation systems, including open-source and framework tools, have been applied for multi-athlete and individual tracking, performance assessment, and injury prevention. Additionally, AI-powered systems and generative AI for data simulation enhance strategy planning and training efficiency. This review provides a comprehensive overview of AI applications in human movement assessment, highlighting methodological approaches, practical implementations, and emerging technologies. Understanding the capabilities and limitations of these systems helps optimize human movement assessment and support data-driven decisions.
Edriss et al. (2026) studied this question.