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Monitoring Adherence to Post-Sternotomy Movement Precautions: A Computer Vision and Generative AI Approach
Davide Ferrari1, Giulia Besana1, Anna Dotti1
1Department of Management, Information and Production Engineering, University of Bergamo, Dalmine (Bergamo), Italy.
Background:
Adherence to movement precautions following sternotomy is essential for sternal healing, but patients often find it difficult to maintain the correct behavior.
Methods:
This study introduces a real-time computer vision-based system to evaluate movement compliance with the post-sternotomy precautions protocol. A YOLOv11 object detection model was trained using a dataset comprising real images and AI-generated images. A secondary monitoring algorithm was developed to use YOLO inference results to classify the whole action rather than a single frame.
Results:
The integration of synthetic data significantly enhanced YOLO model performance, achieving a mAP50 of 80.3%. In real-time validation, the monitoring algorithm correctly classified 85% of non-compliant actions without misclassifying any compliant actions.
Conclusion:
This work demonstrates the feasibility of a low-cost, non-invasive solution for monitoring post-sternotomy precautions. Furthermore, the use of Generative AI proved effective in overcoming data scarcity.
