A CNN-based personalized system for attention detection in wayfinding tasks. (October 2020)

• Record Type: Journal Article
• Authors: Wang, Yanchao; Shi, Yangming; Du, Jing; Lin, Yingzi; Wang, Qi
• Is Part Of: Advanced engineering informatics. Volume 46(2020)
• Publication Date: 2020-10
• Subjects: Wayfinding task -- Virtual reality (VR) -- Electroencephalography -- Deep neural network -- Convolutional neural networks -- Attention detection; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101180
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Electroencephalography measures human responses to wayfinding scenarios. Convolutional neural networks accurately detect human attention. The models classify brain activities in memorizing and recall. Personalized models are needed in building a cognition-driven intelligent system. Abstract: 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.