Hierarchical Contrastive Representation for Accurate Evaluation of Rehabilitation Exercises via Multi-View Skeletal Representations

Rehabilitation training is essential for the recovery of patients with conditions such as stroke and Parkinson's disease. However, traditional skeletal-based assessments often fail to capture the subtle movement qualities necessary for personalized care and are not optimized for scoring tasks. To address these limitations, we propose a hierarchical contrastive learning framework that integrates multi-view skeletal data, combining both positional and angular joint information. This integration enhances the framework's ability to detect subtle variations in movement during rehabilitation exercises. In addition, we introduce a novel contrastive loss function specifically designed for regression tasks. This new approach yields substantial improvements over existing state-of-the-art models, achieving over a 30% reduction in mean absolute deviation on both the KIMORE and UIPRMD datasets. The framework demonstrates robustness in capturing both global and local movement characteristics, which are critical for accurate clinical evaluations. By precisely quantifying action quality, the framework supports the development of more targeted, personalized rehabilitation plans and shows strong potential for broad application in rehabilitation practices as well as in a wider range of motion assessment tasks.