Rehabilitation movement simulation via joint angle-based generative AI

In recent years, generative models have shown remarkable capabilities in synthesizing realistic human motion, with applications ranging from animation to virtual reality. However, their potential in clinical and rehabilitation settings remains underexplored. In this work, we introduce a conditional diffusion-based generative framework for rehabilitation-oriented motion synthesis, which directly operates on joint-angle representations of full-body movement. Unlike most existing approaches that rely on joint positions, our method generates motion in a clinically meaningful space that explicitly encodes joint range of motion, aligning the generation process with how motor performance is assessed in rehabilitation practice. This design enables subject-independent modeling while improving the interpretability of the generated movements from a clinical perspective. We propose a comprehensive evaluation protocol by combining qualitative and quantitative metrics, including simulation visualizations, similarity analysis, and automated assessment of simulations adherence to users input. Experiments based on cross-subject and leave-one-combination-out settings demonstrate the model's ability to generate plausible, contextually accurate motion sequences, with improved generalization when using joint angle representations, achieving superior performance compared to a position-based approach. Despite limitations due to dataset size and gesture diversity, results support the feasibility of generating rehabilitation-oriented motion simulations, motivating future investigation in personalized rehabilitation scenarios.