mental workload
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2022 ◽  
Vol 193 ◽  
pp. 107977
Julien Audiffren ◽  
Jean-Luc Bloechle ◽  
Jean-Pierre Bresciani

2022 ◽  
Hongfei Zhao ◽  
Jinfei Ma ◽  
Yijing Zhang ◽  
Ruosong Chang

Abstract As self-driving vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated psychological load. This study uses a 2 (driving mode) * 2 (call content complexity) * 6 (driving phase) three-factor mixed experimental design to investigate the effect of these factors on the driver's psychological load by measuring the driver's performance on peripheral visual detection tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental load levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in automatic driving mode, as it increases mental load while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety.

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 535
Mahsa Bagheri ◽  
Sarah D. Power

Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.

2022 ◽  
Vol 8 (2) ◽  
pp. 333-338
Muhammad Qurthuby

Giving excessive workloads causes work stress both physically and psychologically and emotional reactions. The excess workload experienced by drivers and swampers at PT XYZ results in reduced attention at work, decreased work motivation, and decreased skill levels, thus affecting drivers and swampers productivity and the chance of a work accident is very high. This study aims to measure the Mental Workload of Job Driver and Swampers Fuel Tank Using the NASA-TLX Method. The NASA-TLX score obtained 93.8, driver 2  get 83.7, driver 3  91.3, swamper 1  91, swamper 2 89.5, and swamper 3 94.7. Elements of mental workload that are very influential are Mental Demand with a percentage of 22%, Effort 20%, Physical Demand 18%, Own Performance 15%, Frustation Level 15% and Temporal Demand 12%.

Francesco Di Nocera ◽  
Rosa De Piano ◽  
Piero Maggi ◽  
Federica De Falco ◽  
Giovanni Serra

2022 ◽  
pp. 543-566
Ángel Fabián Campoya Morales ◽  
Juan Luis Hernández Arellano ◽  
Elvia Luz González-Muñoz

This chapter presents information about the methods that combine physical and mental workload/fatigue during ergonomic evaluation. The methods were identified through a systematic literature review. The search criteria were done through a literature search in databases like SciFinder, SciELO, ScienceDirect, etc. As result, the following methods are described: Global Load Scale, Multivariate Workload Assessment, Subjective Fatigue Symptoms Test, Fatigue Assessment Scale, Scale of Recovery for Exhaustion of Occupational Fatigue, Scale of Estimated Fatigue-Energy Points, Swedish Occupational Fatigue Inventory, NASA-TLX, Combined Cognitive and Physical Assessment, Laboratory Method of Economics and Sociology of Work, OWL Method, Ergonomic Checklist Method, RENAULT Method, Joyce Method, NERPA Method, ARBAN Method, and MAPFRE Method. As a conclusion, it is possible to affirm that there are some evaluation methods that provide better elements for an accurate evaluation, and others lack basic elements, which causes an incomplete/not accurate evaluation.

2022 ◽  
pp. 1339-1366
Arturo Realyvásquez-Vargas ◽  
Emigdio Z-Flores ◽  
Lilia-Cristina Morales ◽  
Jorge Luis García-Alcaraz

This chapter aims to know the mental workload level and its effects on middle and senior managers in manufacturing companies. The chapter aims to know the mental workload level related to gender, age range, civil status, number of children, years of experience, and worked hours per week. As method, the NASA-TLX method was implemented. This method measures mental workload based on six dimensions: mental demand, physical demand, temporal demand, effort, performance, and frustration level. Data was collected by applying an online questionnaire. Results indicated that some dimensions contributed to mental workload in the following decreasing order: mental demand, temporal demand, effort, performance, frustration level, and physical demand. Similarly, results from mental workload level varied from 55.73 to 64.10. Nevertheless, there was no clear relationship between the gender, age range, civil status, number of children, years of experience, worked hours per week, and mental workload level. Finally, employees manifested mental workload mainly due to stress, mental fatigue, and headache.

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