Stud Health Technol Inform. 2026 May 7;335:191-196. doi: 10.3233/SHTI260083.
ABSTRACT
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.
PMID:42119119 | DOI:10.3233/SHTI260083
