# Driver Identification and Detection of Drowsiness while Driving
## 6 Results  
The final results obtained were recorded in a demo at Nokia's facilities, showing the operation of the client-server architecture. As it is a tool that has to function in real time, the response times are minimal. This demo was carried out in a simulated environment so as not to put people, infrastructure, or other collateral damage at risk. For this, a seat was used as the driver's seat, and various car simulation devices, such as the steering wheel, gearshift, pedals, and the camera were located in the optimal position to record the driver's face. This test environment is shown in Figure 6, where the two 5G modems previously discussed can be seen. The demo showed part of the results discussed above on the Jetson Nano client. Figure 7 shows how an unauthorized person fails to find a match within the vehicle database, preventing them from starting the vehicle. Furthermore, the results of the user identification section are displayed, including the user's name and their eye state, as shown in Figure 8.  
For the control client, the KPI for authorized and unauthorized users, as well as driver status monitoring (asleep/awake), are presented. Figure 9 shows a user with identifier 404, which means that he is an unauthorized user who cannot start the car, leading to an instant program termination. The figure also shows a user with identifier 3, indicating an authorized user. The driver's identification variable is set to 0, allowing the user to start  
Figure 8: Person state result. Eyes open/closed. Figure 6: Test environment. Figure 7: Result of unauthorized person trying to start the vehicle. the car. After the identification, real-time drowsiness monitoring begins, and the sleep state variable toggles between 0 (awake) and 1 (detecting drowsiness). Given the results obtained, we have achieved the proposed objectives and generated a program with client-server architecture that can be placed in any vehicle to be accessed only by authorized persons and in which drowsiness monitoring is performed in real time to avoid not only material damage but also accidents and physical damage. In order to analyze the amount of data sent by the client to the server, the iftop command was used to test different configurations in the client code to see how it affect network consumption. Mainly, two parameters were modified: the number of frames per second that the client sends to the server, varying between 30, 15, and 5 FPS, and the resolution of the image captured by the camera, which is 1280 \(\times\) 480 pixels, maximum, and 640 \(\times\) 240, minimum. As can be seen in Table 2, the value of megabits per second (Mbs) sent by the client varies between 22.9 Mbs and 1.08 Mbs. In order to avoid network saturation, it was decided to leave a default image resolution of 1280 \(\times\) 480 and 5 FPS. Figure 9: Authorized/Unauthorized person result. In Figure 10, the bar chart effectively visualizes the relationship between FPS, image resolution, and network consumption in megabits per second (Mbs), as derived from the client-server data transmission tests. Each pair of bar clusters represents a specific image resolution 1280 \(\times\) 480 pixels and 640 \(\times\) 240 pixels. Within each cluster, individual bars correspond to different FPS settings: 30, 15, and 5 FPS. The height of each bar indicates the amount of data transmitted at that particular combination of resolution and FPS. This graphical layout allows for an immediate visual comparison across different configurations, highlighting the proportional increase in network usage with higher FPS and the more considerable data demands of the higher resolution setting. It serves as a compelling visual tool to complement the tabulated data, making the patterns and relationships in the data readily apparent and easily comprehensible. In terms of system limitations, various aspects have been considered. In order to assess the effectiveness of the identification phase, the data collected were analyzed to gauge test success. These data were categorized into three outcomes:  
1.