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A model to predict fatigue degraded performance

机译:一种预测疲劳降级性能的模型

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Maintaining alertness has always been a critical problem for personnel on shift work supporting around the clock operations. Yet vigilance on duty is increasingly threatened by a reliance on sophisticated electronic systems with the attendant increase in the cognitive workload, by a greater reliance on protracted, continuous operations, particularly at night, and by the current emphasis on reduced staffing. An empirically based mathematical algorithm was developed for a tool that can be used to manage fatigue on duty. The algorithm estimates fatigue resulting from extended duty days and fragmented or reduced sleep. It was validated by predicting the observed outcome from a separate study of performance degradation by fatigue (r=0.87; p0.02). The algorithm would be useful in many human system integration problems such as predicting which crew duty schedules would produce the least fatigue. An example is described comparing shift schedules during two weeks of maritime activity.
机译:维持警报始终是在时钟运营周围支持的班次工作人员的关键问题。然而,随着认知工作量的扶持性的复杂电子系统依赖于认知工作量的依赖,越来越依赖于认知工作量的依赖,越来越依赖于持续的,持续的操作,特别是在晚上,并通过目前的重点对减少人员的重点来越来越威胁。为可用于管理疲劳的工具开发了一种经验基础的数学算法。该算法估计延长的占空天和碎片或睡眠减少产生的疲劳。通过预测通过疲劳的性能降解的单独研究预测观察结果来验证(R = 0.87; p> 0.02)。该算法在许多人类系统集成问题中是有用的,例如预测哪个机组人员占空比将产生最不疲劳。描述了在海事活动的两周内比较移位时间表的示例。

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