Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker
Document Type
Conference Proceeding
Publication Date
1-1-2023
Publication Title
Lecture Notes in Networks and Systems
Volume
700 LNNS
First Page
86
Keywords
cognitive load, eye-tracker, human-machine automation, Machine Learning, n-back tasks
Last Page
95
Abstract
Advanced Driver Assistance Systems (ADAS) and other human-machine automation systems should be able to accurately recognize and adapt to the cognitive load of the user. For effective human-machine automation, it is important to develop techniques to automatically predict the cognitive load, based on data from non-invasive and low-cost sensors, such as eye-trackers. In this paper, we investigate the use of machine learning (ML) to classify the cognitive load of a participant performing n-back tasks. The ML models are trained using a large number of raw eye-tracking metrics. Our results demonstrate that tree-based algorithms are able to quickly predict cognitive load with a high degree of accuracy compared to other methods, indicating their potential usefulness for real-time applications.
DOI
10.1007/978-3-031-33743-7_7
ISSN
23673370
E-ISSN
23673389
ISBN
9783031337420
Recommended Citation
Collins, Aaron; Pillai, Parthana; Balasingam, Balakumar; and Jaekel, Arunita. (2023). Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker. Lecture Notes in Networks and Systems, 700 LNNS, 86-95.
https://scholar.uwindsor.ca/computersciencepub/87
