Analysing the Impact of Machine Learning to Model Subjective Mental Workload: A Case Study in Third-Level Education

Moustafa, Karim; Longo, Luca

doi:10.1007/978-3-030-14273-5_6

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…Self-report measures, often referred to as subjective measures, involve a participant or “subject” who usually provides qualitative and/or quantitative reports concerning his/her personal experience while performing either a primary, or secondary task or both (Moray, 1982 ; Vidulich, 1988 ; Nygren, 1991 ; DiDomenico and Nussbaum, 2008 ; Moustafa and Longo, 2018 ). In many self-report measures, a user is asked to answer a pre and/or a post-task questionnaire.…”

Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

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Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

Self Cite

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“…Mental workload has been identified as a crucial element that significantly affects the human brain's performance [2]. As a concept, it has been widely used in the design and evaluation of complex human-machine systems and environments, including those for operating aircraft [4], operating trains and vehicles [5] [6], different human-computer and brain computer interfaces [7] [8] [9] and educational contexts [10] [11] [12]. Over the past two decades, there has been an increase in interest in studying mental workload due to the emergence of a number of technologies that need users to operate at various levels of cognition and in a variety of environments.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Islam¹,

Sarkar²,

Hossain³

et al. 2023

JBM

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Mental workload plays a vital role in cognitive impairment. The impairment refers to a person's difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experimentbased EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier's accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

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“…Mental workload has been identified as a crucial element that significantly affects the human brain task performance [2]. As a concept, it has been widely used in the design and evaluation of complex human-machine systems and environments, including those for operating aircraft [4], operating trains and vehicles [5], [6], different human-computer and brain computer interfaces [7]- [9] and educational contexts [10]- [12]. Over the past two decades, there has been an increase in interest in studying mental workload due to the emergence of a number of technologies that need users to operate at various levels of cognition and in a variety of environments.…”

Section: Introductionmentioning

confidence: 99%

EEG workload estimation for simultaneous task using deep learning algorithm

Islam¹,

Hossain²,

Ahmed³

et al. 2023

World J. Adv. Res. Rev.

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Mental workload plays a vital role in cognitive impairment refers to a person’s trouble of remembering, receiving new information, learning new things, concentrating, or making decisions that affect seriously in their everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was discussed with 45 subjects for multitasking mental workload estimation using an open access preprocessed EEG dataset. Discrete wavelet transforms (DWT) was used for feature extraction and selection. Scalogram formation was performed for data image conversion form from extracted data. AlexNet classification algorithm was used to classify dataset for low and high workload conditions including some other CNN models to show the comparative study of them. The comparative studies of the used classifier’s accuracy along with other performance parameters with the literature expresses the validation for the study which crossed state-of-the art methodologies in the literature by 77.78 percent.

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Analysing the Impact of Machine Learning to Model Subjective Mental Workload: A Case Study in Third-Level Education

Cited by 8 publications

References 43 publications

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human Mental Workload: A Survey and a Novel Inclusive Definition

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

EEG workload estimation for simultaneous task using deep learning algorithm

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