# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 3 Experiments and Results
### Eye Movement Tracker
#### 3.4.2 Actual Eye Movement Tracking  
The processing flow of actual eye movement tracking closely mirrors that of the physical simulation model. It also contains four parts: frame extraction, target detection and tracking, trajectory calculation, and result analysis. In order to reduce noise and jitter, we applied smoothing algorithms to optimize the eye movement trajectory. The pre-recorded eye videos were processed using the tracker to analyze eye movement trajectories. As illustrated in Figure 6, one of the eye trajectory prediction results is presented. The selected video segment, spanning approximately 400 ms, was decomposed into 12 frames, exhibiting smooth trajectories and high confidence levels. Figure 5: Physical simulation model and software interface for trackers. (**a**) Eye movement simulation system, including computer, camera, and physical models. (**b**) The 3D printed platform for eye movement simulation, in which the white moving ball represents the eye and the yellow elastic film represents the eyelid. (**c**) Tracker software interface. Confidence and IoU thresholds can be adjusted for better tracking, and the information bar at the bottom of the interface displays information about the movement of the tracked target. A 5 s video (150 frames) was evaluated using the tracker, with all frames labeled with ground truth bounding boxes. The predicted bounding box for each frame of the video was compared with the ground truth, and the pixel distance between the centers of the two boxes was calculated. The width of the photographed eye portion is 50 mm, covering 640 pixels in the image. According to this rule, we can calculate the distance error of the tracker. As illustrated in Figure 7 regarding the distance error of the eye movement tracker, among the 150 frames, the target was undetected in two frames, while the remaining 148 frames exhibited an average distance error of 0.77 mm. Additionally, 90% of the distance errors were below 1.67 mm, with the maximum distance error not exceeding 6 mm. We further applied the tracker to analyze real-time eye videos for trajectory prediction. The tracker ran smoothly at the camera acquisition rate of 30 frames per second. The experimental results demonstrate that the tracker achieves exceptional performance in eye movement detection, generating smooth tracking trajectories that accurately capture eye movement characteristics across various directions. Through testing on horizontal, vertical, and oblique eye movements, the tracker not only precisely identifies the direction of eye movements but also computes the corresponding movement distance and velocity parameters in real time. Figure 6: Eye movement trajectories in a pre-recorded video. (**a**) The trajectories were drawn at the end of 400 ms. (**b**) Target detection was independently conducted for each of the 12 frames. To objectively evaluate the tracker's performance, we recruited an independent healthy adult subject (a 23-year-old male) whose eyelid data were not included in the dataset. We carefully installed the acquisition system, maintaining a consistent 20-30 mm distance between the camera and the center of the subject's eyelid throughout testing. To minimize potential interference from head movements, we employed a chin rest stabilization system to reduce motion artifacts that might compromise data quality. During testing, the subject performed precisely controlled eye movements with closed eyelids, sequentially executing eight primary directions: horizontal (left/right), vertical (up/down), and four diagonal directions (upper left, upper right, lower left, and lower right). Each directional movement was systematically repeated ten times to establish reliable performance metrics. The tracking system operated at 30 frames per second, capturing and displaying real-time motion trajectories through a visualization window that presented a continuous image sequence clearly depicting the subject's eye movement patterns.