# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 5 Conclusions  
In this paper, we present an eye movement detection system based on the YOLO model, specifically designed for closed-eye conditions. Utilizing a miniature camera to capture images of eyes covered by eyelids, followed by model training and tracker development, the system achieves real-time detection of eye movements under closed-eye scenarios.  
Compared with traditional eye-tracking techniques, our method has several advantages:  
* It enables high-precision eye movement detection under closed-eye conditions, addressing the limitations of traditional eye trackers in detecting eye movements during blinking or eye closure;
* The method exhibits high real-time performance, making it suitable for real-time monitoring applications in both clinical and research settings;
* The proposed method eliminates the need for complex hardware, demonstrating high practicality and generalizability.  
Although the system is promising, it has only been tested and analyzed for potential applications in the laboratory. To realize the value of eye-tracking applications in the closed-eye state, it is necessary to correlate the collected eye movement data with physiological and pathological information. This requires further collaboration with hospitals to seek guidance and advice from doctors.  
Our dataset collected eyelid images from only five healthy subjects due to limited ability. While this suffices for our present laboratory investigation, substantial data collection for training purposes is necessary in order to improve the model's generalizability in future applications.  
The detection system remains susceptible to interference from head motion, which compromises tracking accuracy. To enhance performance, improvements could be made either to the hardware design of the acquisition system or the robustness of the detection algorithms. Potential hardware enhancements might involve integrating an inertial measurement unit for active head motion compensation or employing cameras with higher frame rates and wider field-of-view lenses to maintain tracking during head displacement. On the algorithmic side, implementing advanced filtering techniques or motion correction algorithms could help distinguish genuine eye movements from head motion artifacts.  
Furthermore, to ensure reliable telemedicine applications, several aspects need to be addressed. These include developing more compact wearable acquisition devices, enhancing power efficiency for prolonged monitoring, and establishing robust data transfer protocols for remote diagnosis, all while maintaining patient privacy through the minimized transmission of raw data.  
In the future, this system can be further optimized and applied in medical diagnosis and human-computer interaction, promoting the widespread adoption and advancement of eye-tracking technology.  
Conceptualization, Y.Z. and S.Z.; methodology, Y.Z. and S.Z.; software, S.Z. and J.H.; validation, S.Z.; formal analysis, S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, Y.Z., S.Z. and J.H.; visualization, S.Z.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study as it posed no risk to human subjects and did not involve sensitive personal information or commercial interests.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.