以使用为中心的交互系统设计中操作员工作量的模型驱动估计

D. K. B. Ismail, Olivier Grivard
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引用次数: 2

摘要

操作人员工作量的测量是面向使用的专业系统设计的一个重要方面。在航空电子、空中交通管理或任务系统等领域,能够量化操作员在压力下的工作量,以及在潜在苛刻的身体和精神条件下的工作量,对于预测过载和防止人为错误是强制性的。目前的工作量估计方法主要依赖于仿真实验,这种方法已经证明了它在识别不良系统和/或用户界面设计方面的效率。即使不能期望完全避免实验,考虑到工作负载计算问题的复杂性,工作负载的先验估计可能是一种有趣的工具,可以预先验证设计,以便在实验阶段节省一些时间,并促进对实验期间出现的过载情况的分析。已经提出了对工作量进行先验测量的各种方法:基于成绩的、生理的和主观的测量。虽然工作量的表现和生理测量可能更精确,但主观测量更实际,更容易使用,成本更低。由于这些原因,它们已被应用于许多复杂的领域。在文献中,操作员的经验、技能和培训水平已被确定为重要的人为因素。然而,在工作量估计的上下文中,这些参数还没有被深入分析。在本文中,我们基于对分配给人工操作员的任务的分析,开发了一个预测工作量模型。我们建议使用任务、人类演员、人类角色、知识和能力的心理表征。然后,我们建议根据操作员的经验和培训、随时间的负载和任务复杂性来估计操作员的工作量。在法国美杜莎项目的背景下,我们的方法以空中海上监视用例为例进行了说明。
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Model-driven estimation of operators’ workload for usage centred design of interactive systems
The measurement of the operators' workload is an important aspect of usage-oriented design of professional systems. In domains such as avionics, air traffic management or mission systems, being able to quantify the operators' workload under stress, and in potentially demanding physical and mental conditions, is mandatory to anticipate overload and prevent human errors. Current approaches to workload estimation rely mainly on experimentation in simulation as an approach that has proven its efficiency for the identification of bad system and/or user interface design. Even if one cannot expect to totally avoid experimenting, given the complexity of the issue of workload computation, a priori estimation of workload might be an interesting tool to pre-validate a design in order to save some time in the experimentation phase and facilitate the analysis of overload situations that appear during experimentation. Various approaches to the a priori measurement of workload have been proposed: performance-based, physiological and subjective measures. Although performance and physiological measures of workload may be more precise, subjective measures are more practical, easier and less costly to use. For these reasons, they have been applied to many complex domains. The experience, the skills and the level of training of the operator have been identified in the literature as being important human factors. Nevertheless, these parameters have not been deeply analyzed in the context of workload estimation. In this paper, we develop a predictive workload model based on the analysis of the tasks assigned to a human operator. We propose to use mental representations of tasks, human actors, human roles, knowledge and abilities. We then propose to estimate the operator's workload with reference to his experience and training, the load over time and the task complexity. Our approach is illustrated on an airborne maritime surveillance use-case, in the context of the French Medusa project.
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