Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

Y Ding, Y Cao, VG Duffy, Y Wang, X Zhang - Ergonomics, 2020 - Taylor & Francis
This study attempted to multimodally measure mental workload and validate indicators for
estimating mental workload. A simulated computer work composed of mental arithmetic …

Beyond subjective self-rating: EEG signal classification of cognitive workload

P Zarjam, J Epps, NH Lovell - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Cognitive workload is an important indicator of mental activity that has implications for
human-computer interaction, biomedical and task analysis applications. Previously …

Effects of display curvature and task duration on proofreading performance, visual discomfort, visual fatigue, mental workload, and user satisfaction

S Park, G Kyung, D Choi, J Yi, S Lee, B Choi, S Lee - Applied ergonomics, 2019 - Elsevier
This study examined the effects of display curvature and task duration on proofreading
performance, visual discomfort, visual fatigue, mental workload, and user satisfaction. Five …

Assessing cognitive workload using cardiovascular measures and voice

EH Magnusdottir, KR Johannsdottir, A Majumdar… - Sensors, 2022 - mdpi.com
Monitoring cognitive workload has the potential to improve both the performance and fidelity
of human decision making. However, previous efforts towards discriminating further than …

Human performance modeling and its uncertainty factors affecting decision making: a survery

N Li, J Huang, Y Feng - Soft Computing, 2020 - Springer
This paper introduces the background and connotation of human performance modeling
(HPM), HPM models, and the application of artificial intelligence algorithms in HPM. It deeply …

Objective-analytical measures of workload–the third pillar of workload triangulation?

C Rusnock, B Borghetti, I McQuaid - … Conference, AC 2015, Held as Part …, 2015 - Springer
The ability to assess operator workload is important for dynamically allocating tasks in a way
that allows efficient and effective goal completion. For over fifty years, human factors …

Cognitive workload classification using cardiovascular measures and dynamic features

EH Magnusdottir, KR Johannsdottir… - 2017 8th IEEE …, 2017 - ieeexplore.ieee.org
Monitoring cognitive workload has the potential to improve performance and fidelity in
human decision making through a real-time monitoring model. Multiple studies have shown …

Stepwise logistic regression, hierarchical logistic regression, CART and Naïve Bayes for predicting learners' numeracy test results

TV Montshiwa, T Botlhoko - 2022 - researchsquare.com
The prediction of early childhood numeracy skills development is often studied by
determining the learner's performance in a numeracy test. It is an important study area since …

Retweet Prediction Based on User-Based, Content-Based, and Time-Based Features Using ANN Optimized with GWO

IA Rachman, J Jondri - KLIK: Kajian Ilmiah Informatika dan …, 2023 - djournals.com
Social media has emerged as immensely popular and favored platforms among the masses
today. Twitter, being one of the most renowned social media platforms, allows users to …

Can Mental Workload in EEG Tasks Be Classified Using Machine Learning Algorithms?

S Bellamy - 2020 - search.proquest.com
Measurement of mental workload is an important problem in human computer interaction
(HCI) circles. With the rapid development of technology in the field of HCI, there is a need for …