Automated health monitoring system using YOLOv8 for real-time device parameter detection.

Nowadays, monitoring the health of elderly people at home or patients at the hospital on a regular basis is becoming necessary. Unfortunately, peer-to-peer treatment may require a longer time based on the availability of the doctors. In addition, it is practically impossible to go to hospitals for health checkups almost every day of the week. Hence, this research proposes an idea that can automate these processes without decreasing efficiency and reducing manual labor by integrating a healthcare system with the cyber layer to execute the automation processes. Previous text and image recognition studies used different machine learning and deep learning algorithms. However, in this study, an optical character recognition method ‛YOLO V8' is used, which provides a faster detection speed than other methods. The target was to retrofit biomedical devices such as blood pressure monitoring machines, digital thermometers, etc. using image processing techniques. To train the'YOLOv8' model, we have utilized two distinct image datasets that we have developed. The model showed an accuracy of 99.5% in detecting areas of concern on medical devices. Later, for recognition of values of different parameters from those devices a Convolutional Neural Network model is used, which confirms real-time validation employing 1000 images from different medical equipment. An accuracy of 99.7% has been achieved using this method. In the future, other medical devices such as heart rate monitors, pulse oximeters, etc. can be included in this system.