基于混合模糊模型的操作员认知退化和损伤预测

IF 1.4 Q4 ERGONOMICS
Nikolay Alekseevich Korenevskiy, R. Al-kasasbeh, Fawaz Shawawreh, T. Ahram, S. Rodionova, Mahdi Salman, S. Filist, Manafaddin Namazov, A. Shaqadan, Maksim Ilyash
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引用次数: 1

摘要

摘要预测人机系统操作员的认知功能障碍是一个复杂的过程。机器操作工人的注意力和记忆的认知功能受到负面影响。获得认知功能障碍的准确预测为更好地设计机器和改善操作员健康提供了附加值。本研究展示了一个利用混合模糊决策规则的预测模型。这些模型使用健康指标来衡量生物活性点的能量失衡、心理-情绪压力、疲劳和功能储备(FR)水平。我们评估了信息丰富的人机系统操作员的注意力特性,如集中度、体积、选择性、可切换性、分布和稳定性。专家对所获得的数学模型的置信度超过0.85。在具有代表性的对照样本上测试了该专业最脆弱的注意力集中度(CA)的预测质量,结果表明,诊断敏感性、诊断特异性、诊断效率、阳性和阴性结果的预测显著性等决策质量指标超过0.85。所开发的模型被证明可用于现代心理学和心理生理学评估的各种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of operators cognitive degradation and impairment using hybrid fuzzy modelling
Abstract Prediction of cognitive dysfunctions in operators of human–machine systems is a complex process. The cognitive functions of attention and memory are negatively impacted in machine operation workers. Obtaining an accurate prediction of cognitive dysfunctions provides added value to better design machines and improve operator health. This research demonstrates a prediction model utilising hybrid fuzzy decision rules. The models use health indicators that measure energy imbalance of biologically active points, levels of psycho-emotional stress, fatigue and functional reserve (FR). We assess properties of attention as concentration, volume, selectivity, switchability, distribution and stability in operators of information-rich human–machine systems. Expert confidence in the obtained mathematical models exceeds the value of 0.85. The prediction quality was tested on representative control samples for the most vulnerable property of concentration of attention (CA) for this profession, and it was shown that such indicators of decision-making quality as diagnostic sensitivity, diagnostic specificity, diagnostic efficiency, predictive significance of positive and negative results exceed 0.85. The developed model proved useful for various applications in modern psychology, and psychophysiology assessment.
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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
38
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