# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 1 Introduction  
With the rapid development of modern society, the production modes are constantly being innovated, people are suffering from the overuse of their eyes because they have to spend excessive time facing computers and mobile phones [1]. Our eyes struggle to cope with such a high workload that is causing eye fatigue and even some eye diseases [2]. To reduce eye fatigue, timely measurement of eye fatigue has become an urgent research topic. However, individuals often judge the state of fatigue relying on their subjective feelings which cannot distinguish mental fatigue from eye fatigue. Therefore, it is necessary to evaluate eye fatigue by objective and scientific methods. At present, there are three main ways to measure fatigue, and they mainly focus on mental fatigue. However, there is a lack of investigations on eye fatigue [3]. External cameras were used in the latest fatigue detection method to record the human face. All movements of human heads (such as facial expressions, the direction and amplitude of the head movements, eyelid movement, the direction of the line of sight, etc.) are captured by the cameras and modeled by image processing technology. Fatigue levels would be evaluated according to the image data processing results. The main disadvantage of this method is the low accuracy resulting from the instability of the video signal. It is also easy to be affected by the external environment (such as light, device jitter, and human body movements) [1, 4]. Another method of detecting fatigue is using machine learning to measure the fatigue level comprehensively by collecting multimodal physiological signals as the input of the machine learning model. The multimodal physiological signals mentioned above consist of electroencephalographic (EEG), electrocardiogram (ECG), respiratory signalselectrooculography (EOG), blood pressure, pulse, skin impedance, body temperature, etc. [5; 6]. A major advantage of this method is the relatively high accuracy as a consequence of the comprehensiveness of physiological signals used in this method. Nevertheless, a multitude of devices has to be worn by subjects in this kind of experiment, which is intensely inconvenient and costly. Furthermore, a host of parameters used for the analysis can easily lead to a dimensional disaster of the algorithm. Accordingly, this type of fatigue assessment method is inconvenient in practical use and can only be tested in the laboratory environment. There is also a traditional fatigue detection method that is based on external parameters [7]. In this method, subjects are asked to complete a prescribed series of tasks under some set-up special experimental scenarios (such as driving stimulation) [8]. The level of fatigue is classified by the degree of completion of the tasks. This kind of method has long been used in detecting fatigued driving. However, due to its high experimental complexity, long experiment time, and large differences between subjects, resulting in its poor robustness, therefore, can only be used for special purposes [9]. This section has reviewed the three dominant methods of measuring fatigue. Nevertheless, the above methods are aimed at measuring mental fatigue or physical fatigue. There is practically no specific research on eye fatigue detection. This article presented a system for eye fatigue detection, the algorithm of the system can evaluate the level of eye fatigue only by analyzing the single-channel EOG signal. The self-developed eye fatigue detection device is convenient to use and low in cost. The system can be integrated and interacted with using a mobile phone, which is straightforward for the users to operate, save and check historical data. The problems of inconvenient installation, high cost, and difficulty in using traditional equipment are optimized in this article. Furthermore, the importance and originality of this study are that it explores the link between EOG and eye fatigue and it provides an opportunity to advance the detection of eye fatigue.