Evaluating the robustness of multimodal task load estimation models

A Foltyn, J Deuschel, NR Lang-Richter… - Frontiers in Computer …, 2024‏ - frontiersin.org
Numerous studies have focused on constructing multimodal machine learning models for
estimating a person's cognitive load. However, a prevalent limitation is that these models are …

Biosignal-based recognition of cognitive load: A systematic review of public datasets and classifiers

J Seitz, A Maedche - NeuroIS Retreat, 2022‏ - Springer
Cognitive load is a user state intensively researched in the NeuroIS community. Recently,
the interest in designing neuro-adaptive information systems (IS) which react to the user's …

Decoding working-memory load during n-back task performance from high channel fNIRS data

C Kothe, G Hanada, S Mullen… - Journal of Neural …, 2024‏ - iopscience.iop.org
Objective. Functional near-infrared spectroscopy (fNIRS) can measure neural activity
through blood oxygenation changes in the brain in a wearable form factor, enabling unique …

AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients

K Ćosić, S Popović, M Šarlija, I Kesedžić… - Frontiers in …, 2021‏ - frontiersin.org
The COVID-19 pandemic has adverse consequences on human psychology and behavior
long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and …

Research on mental load state recognition based on combined information sources

H Wang, X Zheng, T Hao, Y Yu, K Xu… - … Signal Processing and …, 2023‏ - Elsevier
Whether from a theoretical research perspective or a practical situation, mental loads lead to
lower productivity, worse job quality, and industrial accidents, and evaluating the mental …

Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection

T Hao, K Xu, X Zheng, J Li, S Chen, W Nie - Biomedical Signal Processing …, 2024‏ - Elsevier
The psychophysiological state of workers in high-risk industries can affect their work
efficiency and overall well-being, representing one of the major causes of catastrophic …

Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning

MA Khan, H Asadi, MRC Qazani, CP Lim… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Motion simulators allow researchers to safely investigate the interaction of drivers with a
vehicle. However, many studies that use driving simulator data to predict cognitive load only …

Cognitive load assessment of active back-support exoskeletons in construction: A case study on construction framing

A Akanmu, A Okunola, H Jebelli, A Ammar… - Advanced Engineering …, 2024‏ - Elsevier
Active back-support exoskeleton has emerged as a potential solution for mitigating work-
related musculoskeletal disorders within the construction industry. Nevertheless, research …

Adaptive training on basic AR interactions: Bi-variate metrics and neuroergonomic evaluation paradigms

S Vyas, S Dwivedi, LJ Brenner, I Pedron… - … Journal of Human …, 2024‏ - Taylor & Francis
Augmented Reality (AR) training is a cost-effective and safe alternative to traditional
instructional methods. However, training novices in basic mid-air AR interactions remains …

Gaze-based Metrics of Cognitive Load in a Conjunctive Visual Memory Task

D Bacchin, NA Gehrer, K Krejtz, AT Duchowski… - Extended Abstracts of …, 2023‏ - dl.acm.org
Measurement of Cognitive Load (CL) is of considerable importance to Human-Computer
Interaction (HCI) as it relates to ease of learn-and usability. Numerous methods have been …