In this article, a new type-2 fuzzy-based modeling approach is proposed to assess human operators’ psychophysiological states for both safety and reliability of human–machine interface systems. Such a new modeling technique combines type-2 fuzzy sets with state tracking to update the rule base through a Bayesian process. These new configurations successfully lead to an adaptive, robust, and transparent computational framework that can be utilized to identify dynamic (i.e., real time) features without prior training. The proposed framework is validated on mental arithmetic cognitive real-time experiments with ten participants. It is found that the proposed framework outperforms other paradigms (i.e., an adaptive neuro-fuzzy inference system and an adaptive general type-2 fuzzy c-means modeling approach) in terms of disturbance rejection and learning capabilities. The proposed framework achieved the best performance compared to other models that have been presented in the related literature. Therefore, the new framework can be a promising development in human–machine interface systems. It can be further utilized to develop advanced control mechanisms, investigate the origins of human compromised task performance, and identify and remedy psychophysiological breakdown in the early stages.

中文翻译：

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一种用于实时人机界面心理生理状态建模的自适应通用 2 类模糊逻辑方法

在本文中，提出了一种新的基于 2 类模糊的建模方法，以评估人类操作员的心理生理状态，以确保人机界面系统的安全性和可靠性。这种新的建模技术将类型 2 模糊集与状态跟踪相结合，通过贝叶斯过程更新规则库。这些新配置成功地产生了一个自适应的、健壮的和透明的计算框架，该框架可用于识别动态（即实时）特征而无需事先训练。所提出的框架在十名参与者的心算认知实时实验中得到验证。发现所提出的框架优于其他范式（即，自适应神经模糊推理系统和自适应通用 2 型模糊 c 均值建模方法）在干扰抑制和学习能力方面。与相关文献中提出的其他模型相比，所提出的框架实现了最佳性能。因此，新框架可能是人机界面系统的一个有前途的发展。它可以进一步用于开发先进的控制机制，调查人类任务表现受损的起源，并在早期阶段识别和纠正心理生理崩溃。