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Fatigue Detection Caused by Office Work with the Use of EOG Signal
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2020-12-15 , DOI: 10.1109/jsen.2020.3012404
Marcin Kolodziej , Pawel Tarnowski , Dariusz J. Sawicki , Andrzej Majkowski , Ramigiusz J. Rak , Aleksandra Bala , Agnieszka Pluta

Although the psychophysiological signs of fatigue are well known, automatic methods for the detection of fatigue in employees in specific working conditions are still lacking. Many people do repetitive work on computers and become fatigued; therefore, the detection of fatigue in employees can help prevent accidents and increase their work efficiency. In this article, we propose an algorithm for the effective detection of fatigue which is based only on electrooculographic (EOG) signal. Three features were assessed: blink duration, blink amplitude, and time between blinks. To cause fatigue, the ${N}$ -back test, lasting for 60 minutes, was carried out. The article presents the research results for 24 users. The effectiveness of the proposed system was measured by the accuracy of classification. The average classification accuracy was 0.93 for user-dependent mode and 0.89 for user-independent mode. The results of the conducted experiments indicated that assessing the three proposed features can help in the effective detection of fatigue in users.

中文翻译:

使用EOG信号进行办公室工作引起的疲劳检测

尽管疲劳的心理生理迹象众所周知,但仍然缺乏在特定工作条件下检测员工疲劳的自动方法。许多人在电脑上重复工作并变得疲倦;因此,检测员工的疲劳度有助于预防事故,提高工作效率。在本文中,我们提出了一种有效检测疲劳的算法,该算法仅基于眼电 (EOG) 信号。评估了三个特征:眨眼持续时间、眨眼幅度和眨眼之间的时间。为了引起疲劳,进行了持续 60 分钟的 ${N}$ -back 测试。本文介绍了 24 位用户的研究结果。所提出的系统的有效性是通过分类的准确性来衡量的。平均分类精度为0。93 用于用户依赖模式,0.89 用于用户独立模式。进行的实验结果表明,评估三个提出的特征可以帮助有效检测用户的疲劳。
更新日期:2020-12-15
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