## 4 Results and Discussion
### Evaluation of mHealth Application  
The outcomes of the model's detection process include bounding boxes for each defined class. Hence, further processing within the mHealth application is required to categorize and identify the detected digits as blood pressure values: systolic pressure, diastolic pressure, and pulse rate. Subsequently, an evaluation of these processes was also necessary to assess their effectiveness and accuracy.  
To verify its performance, the model was tested on 40 samples of blood pressure measurements. These samples included 30 images from a test dataset and 10 images directly captured from a digital blood pressure monitor. The inclusion of 10 directly captured images was intended to validate that the model had not overfit. Based on the test results, the accuracy of digit grouping was found to be 96.67%, indicating that only 4 out of 120 blood pressure metrics were incorrectly identified.  
To evaluate performance scalability on mobile platforms, inference tests were conducted on two Android smartphones: Smartphone 1, representing a low-end device, and Smartphone 2, representing a mid-range device. The hardware specifications of the devices used are detailed in Table 4. The average inference times were 1976.50 ms for Smartphone 1 and 1867.60 ms for Smartphone 2, both showing approximately a threefold increase compared to the measurement on Google Colab (CPU). This increase is attributed to differences in device specifications between Google Colab as the model development platform and the mobile phone as the mHealth deployment platform. Mobile phones typically have limited computational capabilities compared to the hardware of Google Colab, thus resulting in longer inference times for model execution. However, while there was a performance difference between the two smartphones, the results indicate that even low-end devices are capable of running the model seamlessly, albeit with slightly longer inference times. The performance of the model in terms of accuracy was also the same on both devices.  
Additionally, the model within the mHealth application was tested on five general cases, including LCD color variations, variations in the layout of blood pressure monitor values, the distance and angle of image capture, and image disturbances such as blur and light reflections. A sample of the test results is shown in Figure 10 where the upper panel shows the input images captured from various blood pressure monitors, while the middle panel shows the corresponding output images with digit predictions generated by the proposed deep learning model. The lower panel presents the extracted systolic, diastolic, and pulse values along with the classification category. Based on the results in the table, the model successfully detected blood pressure values across these defined cases with satisfactory performance. For cases a and b, the model accurately recognized blood pressure values across various digital blood pressure monitor layouts. In cases d and e, the model also successfully detected images taken from a distance and slightly blurred, as long as they remained within reasonable limits for manual readability by the average person. The model only failed to detect digits in some images from the test case involving an extreme angle (more than 45 degrees) of image capture, where the angle was too steep for the model to accurately recognize the seven-segment digits. Overall, the mHealth application has proven capable of accurately reading blood pressure measurement results.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Reference** & **Model/Algorithm** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline [PERSON] et al. [8] & Image Processing & 89.70 & 80.00 \\ [PERSON] et al. [26] & CNN & 90.00 & - \\ [PERSON] \& [PERSON] [10] & Random Forest & 98.20 & - \\ This work & YOLOv8s (INT8) & 99.28 & 96.48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison with other works.