当前位置: X-MOL 学术Pers. Ubiquitous Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A framework to estimate cognitive load using physiological data
Personal and Ubiquitous Computing ( IF 3.006 ) Pub Date : 2020-09-27 , DOI: 10.1007/s00779-020-01455-7
Muneeb Imtiaz Ahmad , Ingo Keller , David A. Robb , Katrin S. Lohan

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.



中文翻译:

使用生理数据估算认知负荷的框架

认知负荷已被广泛研究以帮助理解人类的表现。期望监视诸如自动化,机器人技术和航空航天之类的应用中的用户认知负荷,以实现操作安全性并改善用户体验。这可以实现有效的工作负载管理,并且可以帮助避免或减少人为错误。然而,以高精度实时跟踪认知负荷仍然是一个挑战。因此,我们提出了一种通过非侵入式测量来自眼睛和心脏的生理数据来检测认知负荷的框架。我们举例说明并评估参与者参与一项任务的框架,该任务引起不同程度的认知负荷。该框架使用一组分类器来准确预测低,中和高水平的认知负荷。分类器可实现较高的预测准确性。尤其是,随机森林和朴素贝叶斯的成绩最高,分别为91.66%和85.83%。此外,我们发现,尽管右眼和左眼的平均瞳孔直径变化是最突出的特征,但眨眼速度也为这种对低,中和高认知负荷的高精度预测做出了中等重要的贡献。现有的准确性结果大大优于现有方法,并证明了我们的框架在检测认知负荷方面的适用性。

更新日期:2020-09-28
down
wechat
bug