# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 4 Discussion  
Based on the test results across various versions of YOLO and different pre-trained weights, we successfully identified a target detection model with optimal overall performance. The model demonstrated strong results in AP50, precision, recall, F1 score, and inference time. Its performance was further validated through multiple evaluation metrics, including consistent accuracy under varying lighting conditions, high generalization capability, and robust performance. The system demonstrates excellent performance in real-world operation, exhibiting high real-time capability and stability. By loading the trained optimal model weights, the system efficiently predicts the position of the eyeball in each frame. For the image stream captured by the acquisition system (with a camera frame rate of 30 fps), the system seamlessly processes the data and achieves real-time tracking of eye movement trajectories, ensuring high operational efficiency. The system demonstrates robust performance for both the simulated model and actual eye tests. It achieves precise tracking of eye movement trajectories, displaying the eye movement path in real time with smooth and continuous trajectories, free from noticeable jitter or deviation. Furthermore, through quantitative frame-by-frame analysis of pre  
Figure 8: The monocular movement trajectory of the right eye in nine cardinal directions, includes (**e**) primary position, (**b**) supraduction, (**h**) infraduction, (**f**) levoduction, (**d**) devroduction, (**a**) upward and rightward movement, (**g**) downward and rightward movement, (**c**) upward and leftward movement, and (**i**) downward and leftward movement. recorded videos, the system exhibits low eye-position-distance errors, indicating its high resolution. Most significantly, the eye-tracking results from an independent experimenter, who had no involvement in the training process, were accurate, further validating the system's reliability and robustness in real-world application scenarios. Given the system's high accuracy, real-time performance, and adaptability across various testing conditions, it holds significant potential for a wide range of applications. Below is a detailed discussion of its possible use cases. * Augmentative and Alternative Communication: By detecting eyeball movement patterns beneath closed eyelids, this system could enable individuals with ALS to control smart home devices or operate typing systems for external communication, offering a new means of interaction for those with severe motor impairments;
* Surgical Monitoring: During intracranial surgeries, such as tumor resections near the oculomotor nerve, the system could provide real-time monitoring of eyeball movements under closed eyelids. If the nerve is accidentally stimulated, triggering involuntary eye motion, the system would immediately issue an alert, helping surgeons avoid neurological damage;
* Ophthalmic Diagnostics: By analyzing eyeball trajectories across eight primary directions, the system can detect abnormal movement patterns, assisting clinicians in diagnosing paralytic strabismus. Specifically, it could help identify weakened or overactive extraocular muscles, leading to more precise treatment plans;
* Enhancing Eye-Tracking Technology: Current eye-tracking systems only recognize open-eye states or blink movements. Integrating this system would allow the capture of eyeball motion parameters even during eyelid closure, making eye tracking more continuous and comprehensive;
* Monitoring Critical or Comatose Patients: In intensive care units, the system could provide a low-cost, non-invasive, and real-time solution for long-term monitoring of anesthetized or comatose patients.