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This research presents an AI-driven framework for personalized physiotherapy that integrates multi-modal learning, computer vision, and empathetic NLP to enhance assessments and treatment. The framework utilizes an optimized YOLO Pose algorithm for improved keypoint detection and aims to create a unified semantic model for mapping motion data to clinical parameters. Preliminary results indicate improved pose estimation, supporting the potential for clinical validation and the integration of additional modal

Author

VICTOR JESUS GARCIA HERNANDEZ

Language

EN