# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 1 Introduction  
Eye movement not only reflects the process of visual perception but also contains rich physiological and pathological information [1, 2]. The development of eye movement detection technology has experienced an evolution from the early observation and mechanical recording methods to various high-precision methods nowadays [3]. The current key technology research literature is shown in Table 1. These methods, which include electro-oculography, video oculography, infrared oculography, scleral search coil, and magnetic resonance imaging, have their advantages and disadvantages. The current scientific research hotspots about eye movements mainly focus on the open-eye state, paying attention to the indicators of gaze, smooth tracking, and sweeping [4, 5]. Recent studies have explored the use of low-cost, non-invasive human-computer interaction strategies to develop augmentative and alternative communication solutions for individuals with Amyotrophic Lateral Sclerosis (ALS) [6]. For instance, [PERSON] et al. proposed a real-time blink detection approach that requires only a generic webcam and computer, which enables ALS patients to communicate through intentional eye blink patterns [7]. Eye movement detection is important while the eyes are closed. Its application in sleep medicine, neurosurgery, rehabilitation medicine, and other fields is promising. For example, [PERSON] et al. believed that eye movements during eye closure closely reflect changes in arousal and that visual scores (VSs) of slow eye movements (SEM) and rapid eye movements (REM) provide useful information for clinical and basic research [25]. In skull base surgery, eye movement monitoring in the closed-eye state can provide surgeons with critical neurological function information to help assess the state of the brainstem and cranial nerves during surgery, thereby reducing surgical risk [26; 27; 28]. [PERSON] et al. detected small vibrations induced by eye movements using piezoelectric sensors placed on the eyelids for intraoperative monitoring of the extraocular muscle nerves, with experimental results superior to those obtained using electro-oculography and electromyography [29]. For vegetative patients, eye movements in the closed-eye state may become an important basis for assessing their level of consciousness and neurological recovery and even provide potential avenues for communication [30; 31]. Existing eye movement detection methods applicable to closed-eye conditions face several limitations: electro-oculography offers limited accuracy, scleral search coils are invasive, and magnetic resonance imaging requires expensive equipment. These constraints highlight the need for novel approaches combining high accuracy, non-invasiveness, and real-time performance. Recent advances in computer vision, particularly deep learning-based object detection algorithms, have emerged as promising solutions due to their non-contact nature and high precision. In this context, [PERSON] et al. employed the YOLO model for eye and eye-state detection, demonstrating its superior processing speed compared to alternative object detection algorithms [32]. Consequently, in this paper, we propose a YOLO model-based eye movement detection system under closed-eye conditions. The main contributions of the article can be summarized as follows:  
* We built an image acquisition system. A micro-camera was built based on the OV9734 image sensor, and the corresponding acquisition software was created. The compact size of the camera makes it easy to embed into the eye-tracking system, achieving device miniaturization and portability;