## 2 Related Works  
Several studies have investigated methods for accurately detecting and interpreting seven-segment digits, which are commonly used in medical devices to display numerical information. [PERSON] and [PERSON] [9] demonstrated that deep learning-based object detection models such as Faster Region-Based Convolutional Neural Network (Faster R-CNN) and You Only Look Once (YOLO) outperform traditional OCR-based approaches like Tesseract in detecting seven-segment digits on odometers, particularly in challenging image conditions affected by noise, blur, poor lighting, and oblique viewing angles. This result is relevant, as both odometers and blood pressure monitors employ seven-segment displays to present measurements.  
[PERSON] et al. [8] focused specifically on detecting and recognizing seven-segment digits in blood pressure monitors using conventional image-processing techniques. Their method achieved a digit localization F1-score of 80% and a classification accuracy of 89.70%. However, the blob extraction component of the algorithm occasionally misclassified visually similar digits, particularly between 0 and 8, and between 1 and 7. Moreover, the study was limited to model development and did not include deployment or integration into an mHealth application.  
Another related work [10] proposed a mobile application-based system for reading seven-segment digits on various medical devices, including blood pressure monitors, blood glucose meters, and weight scales. The approach utilized computer vision techniques to extract digit features, followed by a random forest classifier for interpretation. The method achieved an overall accuracy of 98.20%. However, the input images used in the study were not realistic for practical use, as they were pre-cropped and contained only a single measurement type. Furthermore, the system faced classification challenges due to insufficient training data, particularly between digits 0 and 8, and performed poorly on blurry images. The model was not embedded within the mobile application but instead deployed on a remote server, which introduced reliance on internet connectivity and additional latency in the detection process.  
In this work, we address the aforementioned limitations by proposing an end-to-end deep learning approach integrated into an mHealth application for accurate, efficient, and scalable digit recognition and measurement-type classification from blood pressure monitor images. The main contributions of this study are as follows:  
* End-to-End Deep Learning Pipeline: We design a unified YOLOv8-based architecture that automatically detects the region of interest (ROI), without requiring manual cropping or selection, and performs accurate digit recognition. Subsequently, these digits are grouped and mapped to the corresponding measurement types (systolic, diastolic, and pulse) through a spatial-based postprocessing mechanism, utilizing the intersections of the bounding boxes.
* Model Optimization for Edge Deployment: To support real-time inference on mobile devices, we apply post-training quantization (PTQ) to compress the model. We evaluate the trade-offs between different YOLOv8 variants (small, medium, large) in terms of precision, recall, F1-score, accuracy, inference time, and model size under both float32 and INT8 configurations.
* Mobile Health Application Integration: We develop a fully functional Android-based mHealth application that integrates the optimized model. The app enables real-time camera capture, digit recognition, and classification, storing results for continuous blood pressure tracking without the need for manual input.