Enhancing Patients Compliance in Home-Based Respiratory Rehabilitation after Cardiac Surgery: A Smartphone Application for Monitoring Spirometer Exercises Using YOLOv8-Pose

Respiratory rehabilitation is essential for patients after cardiac surgery to avoid postoperative pulmonary complications. However, the majority of rehabilitation programs are self-administered at home, which often results in low compliance. This research work aims to develop a smartphone application to support patients during respiratory rehabilitation sessions with a three-ball incentive spirometer. The developed application leverages the smartphone camera and a keypoints detection algorithm to track spirometer exercises, guiding patients through the session. The app also stores quantitative data, making them available to medical staff on a telemedicine web platform. The application relies on the YOLOv8-pose model to detect seven spirometer keypoints, allowing for accurate tracking of ball positions and corresponding inspiration rates. We trained and optimized three YOLOv8-pose models (Nano, Small, and Medium) using a custom dataset and four image input sizes. Each model was evaluated on a test set of real-world scenarios. YOLOv8-pose Nano, with an input size of 192 pixels, was selected for app deployment, achieving a recall of 98.49%, precision of 98.51%, mAP50 of 98.38%, mAP50-95 of 85.26%, and average inference time on a consumer smartphone of 279±73ms. These results demonstrate the potential of using a lightweight YOLOv8-pose model for real-time monitoring and compliance improvement of respiratory rehabilitation.