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A Survey on Fatigue Detection of Workers Using Machine Learning

机译:采用机器学习疲劳检测调查

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In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.
机译:在软件产业中,输出质量基于人类性能,疲劳可能是性能下降的原因。疲劳不仅降低了质量,而且是健康风险因素。睡眠障碍,抑郁和压力都是疲劳的结果,这可能有助于致命问题。本文提出了对不同技术的比较研究,可用于检测在计算机屏幕前花费大量时间的程序员和数据矿工的疲劳。机器学习也可以用于工人疲劳检测,但有一些因素是软件工作者的特点。其中一种因素是屏幕照明。屏幕照明是计算机屏幕或笔记本电脑屏幕的光线,这些屏幕上铸造在工人面上,并使机器学习算法难以提取面部特征。本文介绍了对一般疲劳检测的技术的比较研究,并识别最佳技术。

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