Abstract
Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.
Highlights
Our eyes struggle to cope with such a high workload that is causing eye fatigue and even some eye diseases [2]
Individuals often judge the state of fatigue relying on their subjective feelings which cannot distinguish mental fatigue from eye fatigue
There are three main ways to measure fatigue, and they mainly focus on mental fatigue
Summary
Algorithms 2022, 15, 84. https://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]. Timely measurement of eye fatigue has become an urgent research topic. It is necessary to evaluate eye fatigue by objective and scientific methods. There are three main ways to measure fatigue, and they mainly focus on mental fatigue. 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 easy to be affected by the external environment (such as light, device jitter, and human body movements) [1,4]
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