Monitoring Adherence to Post-Sternotomy Movement Precautions: A Computer Vision and Generative AI Approach.

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.