Interruptions in the working environment cause extra mental workload for the operators, and this phenomenon has garnered significant research attention. This study designed four interruption conditions based on the perceptual and cognitive perspectives of human information processing, using a 2(perceptual primary task and cognitive primary task)*2(perceptual interruption task and cognitive interruption task) factorial design. Multimodal measurement methods were used to evaluate mental workload in different interruption conditions. The results show that when the primary task and the interruption task are different load types, they generate a higher mental workload than the same load type. It can be attributed to the fact that perceptual tasks and cognitive tasks increase mental workload during switching. In addition, based on the multimodal index data, the prediction model of interruption recovery delay time and the classification model of interruption conditions are established, which provides a basis for rational scheduling of work and preventing mental overload.
This study’s results enhance our understanding of interruptions from the perspectives of perception and cognition, providing a more accurate theoretical basis for managing mental workload in interruption conditions. The proposed interruption recovery delay time prediction model and the interruption condition classification model have certain reference values for improving interruption management capabilities.

UNASSIGNED: Cognitive and environmental parameters are among the most important influencing factors in the prevalence of WRMSDs, which have been studied less compared to physical ergonomic in automobile industry.
UNASSIGNED: This study was conducted with the aim of investigating the relationship between environmental and cognitive ergonomics with the prevalence of WRMSDs in an automotive industry.
UNASSIGNED: This study was conducted in 2023 in an automobile company. The sample size was 740 workers. The prevalence of WRMSDs was assessed using the Cornell Musculoskeletal Discomfort Questionnaire. Occupational stress, mental workload, sleep quality, and cognitive failure were assessed by Job Content Questionnaire, NASA-TLX Questionnaire, Pittsburgh Sleep Quality Index, and Cognitive Failure Questionnaire, respectively. Noise were measured by KIMO-DB300 sound analyzer. The intensity of lighting was measured using a Hanger Screen Master illuminance meter. Heat stress was measured by Wet Bulb Globe Temperature (WBGT).
UNASSIGNED: 72.58% reported the musculoskeletal disorders in at least one of their body parts during the past 12 months. The average values of occupational stress, mental workload, sleep quality, and cognitive failure among workers with WRMSDs were higher than the participants without WRMSDs (p-value < 0.05). There was a significant difference between the values of all studied physical harmful factors between the two investigated groups, except thermal stress (p-value < 0.05).
UNASSIGNED: Findings from this study highlight the critical need for a holistic approach that considers both the external work environment and internal cognitive processes to effectively prevent and manage WRMSDs among automobile industry workers.

Understanding the operator\'s cognitive workload is crucial for efficiency and safety in human-machine systems. This study investigated how cognitive workload modulates cardiac autonomic regulation during a standardized military simulator flight. Military student pilots completed simulated flight tasks in a Hawk flight simulator. Continuous electrocardiography was recorded to analyze time and frequency domain heart rate variability (HRV). After the simulation, a flight instructor used a standardized method to evaluate student pilot\'s individual cognitive workload from video-recorded flight simulator data. Results indicated that HRV was able to differentiate flight phases that induced varying levels of cognitive workload; an increasing level of cognitive workload caused significant decreases in many HRV variables, mainly reflecting parasympathetic deactivation of cardiac autonomic regulation. In conclusion, autonomic physiological responses can be used to examine reactions to increased cognitive workload during simulated military flights. HRV could be beneficial in assessing individual responses to cognitive workload and pilot performance during simulator training.

UNASSIGNED: This study aimed to examine the latent profile of nurses\' mental workload (MWL) and explore the influencing factors via a person-centred approach.
UNASSIGNED: From March to July 2023, a quantitative cross-sectional study was carried out to investigate 526 Chinese clinical nurses from five tertiary hospitals in Sichuan Province, China, by using demographic information, the Perceived Social Support Scale, Simplified Coping Skill Questionnaire, and NASA-Task Load Index. Latent profile analyses were performed using Mplus 7.3 software. Pearson\'s chi-squared and logistic regression analysis was done using SPSS 24.0 software.
UNASSIGNED: Three profiles of mental workload were identified based on the nurses\' responses to the mental workload assessment, designated as \"low MWL-high self-rated (n = 70, 13.3%)\", \"moderate MWL (n = 273, 51.9%)\", and \"high MWL-low self-rated (n = 183, 34.8%)\". Based on the analysis of the three subtypes, nurses with working years < 5 years (χ 2  = 12.135, P < 0.05), no children (χ 2  = 16.182, P < 0.01), monthly income < 6000 (χ 2  = 55.231, P < 0.001), poor health status (χ 2  = 39.658, P < 0.001), no psychological training in the past year (χ2 = 56.329, P < 0.001) and suffering from workplace violence (χ 2  = 19.803, P < 0.001) were significantly associated with MWL. Moreover, the multivariate logistic regression analysis showed that negative coping styles (OR = 1.146, 95% CI: 1.060-1.238, P = 0.001) were accompanied by higher MWL while negatively associated with perceived social support (OR = 0.927, 95% CI: 0.900-0.955, P < 0.001).
UNASSIGNED: Our results showed that the MWL of nurses could be classified into three subtypes. Monthly income, health status, psychological training, workplace violence, negative coping style, and perceived social support were the factors influencing MWL. Managers can employ personalised intervention strategies according to the individual characteristics of different subgroups to reduce nurses\' MWL.

Multi-Attribute Task Battery (MATB) is a computerized flight simulator for aviation-related tasks, suitable for non-pilots and available in many versions, including open source. MATB requires the individual or simultaneous execution of 4 sub-tasks: system monitoring (SYSMON), tracking (TRACK), communications (COMM), and resource management (RESMAN). Fully customizable, the design of test duration, number of sub-tasks used, event rates, response times and overlap, create different levels of mental load. MATB can be combined with an additional auditory attention (Oddball) task, or with physiological constraints (i.e., sleep loss, exercise, hypoxia). We aimed to assess the main characteristics of MATB design for assessing the response to different workload levels. We identified and reviewed 19 articles for which the effects of low and high workload were analyzed. Although MATB has shown promise in detecting performance degradation due to increase workload, studies have yielded conflicting or unclear results regarding MATB configurations. Increased event rates, number of sub-tasks (multitasking), and overlap are associated with increased perceived workload score (ex. NASA-TLX), decreased performance (especially tracking), and neurophysiological responses, while no effect of time-on-task is observed. The median duration used for the test is 20 min (range 12-60) with a level duration of 10 min (range 4-15). To assess mental workload, the median number of stimuli is respectively 3 events/min (range 0.6-17.2) for low, and 23.5 events/min (range 9-65) for high workload level. In this review, we give some recommendations for standardization of MATB design, configuration, description and training, in order to improve reproducibility and comparison between studies, a challenge for the future researches, as human-machine interaction and digital influx increase for pilots. We also open the discussion on the possible use of MATB in the context of aeronautical/operational constraints in order to assess the effects combined with changes in mental workload levels. Thus, with appropriate levels of difficulty, MATB can be used as a suitable simulation tool to study the effects of changes on the mental workload of aircraft pilots, during different operational and physiological constraints.

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the \"STEW\" dataset, which consists of two tasks, namely \"No task\" and \"simultaneous capacity (SIMKAP)-based multitasking activity\". We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

The perception of work is closely linked to body reactions that facilitate task performance. Previous studies have shown that psychosocial work factors significantly impact employee health on both psychological and physical levels, though their cross-sectional designs limit causal interpretations. In this study, participants performed sitting and standing tasks under four different levels of mental workload. The NASA-Task Load Index (NASA-TLX) assessed mental workload perception across six dimensions, while Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA) scores evaluated body postures for standing and sitting tasks, respectively. This study examined the effects of alarms, distractions, and time constraints-common psychosocial factors in healthcare environments-on human behavior. We compared NASA-TLX scores with corresponding REBA/RULA scores to evaluate how perceived mental workload affects body postures. One-way ANOVA assessed the impact of experimental conditions on response variables, and Pearson correlation analyses explored the relationships between psychosocial factors and these variables. Results indicated that alarm conditions most negatively impacted mental workload perception and body postures. Temporal demand and effort scores were particularly affected by psychosocial factors in both tasks. Gender influenced physical demand and performance scores (higher in females) for the standing task but did not affect REBA and RULA scores. These findings suggest that organizational and psychosocial factors significantly influence healthcare workers\' behavior, health, and patient safety. Further research is needed to evaluate the specific effects of psychosocial factors on both physical and mental workload to understand the relationship between overall task workload and occupational disorders.

Recent studies have focused on accurately estimating mental workload using machine learning algorithms and extracting features from physiological measures. However, feature extraction leads to the loss of valuable information and often results in binary classifications that lack specificity in the identification of optimum mental workload. This study investigates the feasibility of using raw physiological data (EEG, facial EMG, ECG, EDA, pupillometry) combined with Functional Data Analysis (FDA) to estimate the mental workload of human drivers. A driving scenario with five tasks was employed, and subjective ratings were collected. Results demonstrate that the FDA applied nine different combinations of raw physiological signals achieving a maximum 90% accuracy, outperforming extracted features by 73%. This study shows that the mental workload of human drivers can be accurately estimated without utilising burdensome feature extraction. The approach proposed in this study offers promise for mental workload assessment in real-world applications.
This study aimed to estimate the mental workload of human drivers using physiological signals and Functional Data Analysis (FDA). By comparing models using raw data and extracted features, the results show that the FDA with raw data achieved a high accuracy of 90%, outperforming the model with extracted features (73%).

Highly complex cognitive works require more brain power. The productivity of a person suffers due to this strain, which is sometimes referred to as a mental burden or psychological load. A person\'s mental health and safety in high-stress working conditions can be improved with the help of mental workload assessment. A photoplethysmogram (PPG) signal is a non-invasive and easily acquired physiological signal that contains information related to blood volume changes in the micro-vascular bed of tissues and can indicate psychologically relevant information to assess a person\'s mental workload (MW). An individual under a high MW possesses an increase in sympathetic nervous system activity, which results in morphological changes in the PPG waveform. In this work, a time-frequency analysis framework is developed to capture these distinguishing PPG features for the automatic assessment of MW. In particular, a cross-wavelet coherence (WTC) approach is proposed to extract simultaneous time-frequency information of the PPG during MW relative to the resting PPG. The suggested technique is validated on a publicly available data set of 22 healthy individuals who took part in an N-back task with PPG recording. Under three different fixed window lengths, images are obtained using WTC between PPG records during N-back task activity and rest. The images are used further to obtain PPG classification in two broad classes of low and high MW using a customized pre-trained Inception-V3 model. The best validation and test accuracy of 93.86% and 93.07%, respectively obtained in the window setting of 1200 samples used for WTC image creation.

This letter offers a constructive review of the article \'Influence of the time-task constraint on ocular metrics of semi-elite soccer\' by Luis-del Campo et al. (2023), focusing on methodological considerations and avenues for future research enhancement. The study investigates the impact of task constraints on eye-tracking metrics among semi-elite soccer players, aiming to gauge mental workload during training sessions. While the study presents valuable insights, there are opportunities for methodological refinement. Suggestions include emphasizing sample size determination, randomization of the experimental conditions, and employing robust statistical analyses to mitigate potential biases. Moreover, future studies could benefit from integrating external load measures alongside heart rate monitoring to comprehensively assess training task variations. Despite these considerations, the study underscores the promising application of eye-tracking techniques in evaluating mental workload during soccer training, paving the way for further exploration and refinement of methodologies to enhance player performance assessment and training optimization in the field.