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A CNN-based personalized system for attention detection in wayfinding tasks
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2020-10-05 , DOI: 10.1016/j.aei.2020.101180
Yanchao Wang , Yangming Shi , Jing Du , Yingzi Lin , Qi Wang

Firefighters are often exposed to extensive wayfinding information in various formats owing to the increasing complexity of the built environment. Because of the individual differences in processing assorted types of information, a personalized cognition-driven intelligent system is necessary to reduce the cognitive load and improve the performance in the wayfinding tasks. However, the mixed and multi-dimensional information during the wayfinding tasks bring severe challenges to intelligent systems in detecting and nowcasting the attention of users. In this research, a virtual wayfinding experiment is designed to simulate the human response when subjects are memorizing or recalling different wayfinding information. Convolutional neural networks (CNNs) are designed for automated attention detection based on the power spectrum density of electroencephalography (EEG) data collected during the experiment. The performance of the personalized model and the generalized model are compared and the result shows a personalized CNN is a powerful classifier in detecting the attention of users with high accuracy and efficiency. The study thus will serve a foundation to support the future development of personalized cognition-driven intelligent systems.



中文翻译:

用于寻路任务中注意力检测的基于CNN的个性化系统

由于建筑环境的复杂性,消防人员经常会接触各种格式的海量寻路信息。由于在处理各种类型的信息方面存在个体差异,因此有必要使用个性化的认知驱动智能系统来减少认知负担并提高寻路任务的性能。然而,在寻路任务期间,混合的多维信息给智能系统带来了严峻的挑战,即无法检测并立即吸引用户的注意。在这项研究中,设计了一个虚拟的寻路实验,以模拟受试者记忆或回忆不同寻路信息时的人类反应。卷积神经网络(CNN)设计用于基于实验期间收集的脑电图(EEG)数据的功率谱密度来进行自动注意力检测。比较了个性化模型和广义模型的性能,结果表明,个性化CNN是高效,准确,高效地检测用户注意力的分类器。因此,该研究将为支持个性化认知驱动的智能系统的未来发展奠定基础。

更新日期:2020-10-05
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