Mental workload measures

Functional near-infrared spectroscopy (fNIR) is a highly portable, safe neuroimaging technology that uses light to measure cortical brain activity. We have utilized fNIR to provide objective measures of cognitive workload of certified Air Traffic Controllers while they managed realistic scenarios under typical and emergent conditions. Participants also completed an n-back task, which is a standardized working memory and attention task with four incremental levels of difficulty. In the n-back results, as task difficulty increases, accuracy and speed of the participants decrease monotonically. Blood oxygenation changes, as measured by fNIR, monotonically increased with increasing task difficulty. These findings are in line with earlier studies. Further, fNIR measures were analyzed for comparison between data-based communication and voice-based communication during an air Ayaz, H.

Workload perception was measured in a drone flight training Simulator computerized situation. There has been increasing research in recent years on the topic of Remotely piloted aircrafts (RPA). Eleven participants were tested for workload perception during a drone flight simulator training. Reliability, sensitivity and correlations were studied for the workload scale and its relationship with the simulator training tasks. Overall, there were clear effects of mental demand as showed in the workload perception during the training tasks. Reliability for the workload scale showed good score and sensitivity showed mental demand as the most important factor compared to the other parameters measured obtaining highest correlations with landing tasks and number of errors. In our results, we have seen how the AWT (adapted from NASA-TLX) showed good sensitivity in assessing the mental burden of participants. In our research, participants scoring higher in the mental demand subscale showed greater difficulty finishing training tasks, and also showed longer time delays in performing both training sections of the simulation. These types of tools measuring workload perception and virtual training systems can be used in future research, to see how this cognitive aspect affects piloting skills and its possible safety and training implications.

Abstract

Measurement of workload is a technique to get information about the efficiency and effectiveness of an organizational unit, or office holders through a process of research and assessment. It is conducted by using job analysis, workload analysis or other management techniques. PT. Ultrajaya Milk Industry & Trading Co., Tbk is manufacturer of the food and beverage. The company is always trying to meet the needs of consumers by creating products with high quality and competitive price. The high stress level perceived by Managerial Level and Supervisory Level become an important reason for the Department of Ultra High Temperature (UHT) to conduct workload analysis. By measuring the employees workload, it will be known whether  the workload of an employee is optimal load or overload. This situation will affect the human-performance (employee). Based on calculations by the DRAWS method is yielded the average workload score for Managerial Level of 67.37% (in the overload category), while the dominant workload variable in Central Demand (CD) is 41.60%,  the distribution of the mental workload is 72.60% and physical workload is 27.40%. While for Supervisory Level  is yielded the average workload score of 64.59% (in the overload category) ), where the dominant workload variable in Central Demand (CD) is 33.63%, the distribution of the mental workload is 55.10% and physical workload is 44.90%. Therefore the perceived mental workload of Supervisory Level is still relatively comparable compared to physical workload, and in contrast to the  perceived Managerial Level which is dominated by mental workload.


Abstrak

Pengukuran beban kerja merupakan suatu teknik untuk mendapatkan informasi tentang efisiensi dan efektifitas kerja suatu unit organisasi, atau pemegang jabatan melalui proses penelitian dan pengkajian yang dilakukan dengan menggunakan teknik analisis jabatan, teknik analisis beban kerja atau teknik manajemen lainnya. PT. Ultrajaya Milk Industry & Trading Co, TBk merupakan perusahaan yang bergerak dibidang makanan & minuman yang selalu berupaya memenuhi kebutuhan konsumen dengan membuat produk yang berkualitas tinggi dan harga yang kompetitif. Tingginya stress yang dirasakan Managerial Level dan Supervisory Level menjadi alasan penting bagi Departemen Ultra High Temperature (UHT) untuk melakukan analisis beban kerja. Melalui pengukuran beban kerja pegawai, maka akan diketahui apakah beban kerja seorang pegawai sudah optimal atau sudah berlebihan, dimana akan berpengaruh terhadap hasil kinerja pegawai yang dihasilkan. Berdasarkan hasil perhitungan dengan metode DRAWS diketahui rata-rata skor beban kerja yang diperoleh untuk Managerial Level sebesar 67,37% (pada kategori beban kerja over-load), dimana variabel beban kerja yang dominan adalah Central Demand (CD) sebesar 41,60%, dengan sebaran beban kerja mental sebesar 72,60% dan beban kerja fisik sebesar 27,40%. Sementara untuk Supervisory Level  diperoleh rata-rata skor beban kerja sebesar 64,59% (pada kategori beban kerja over-load), dimana variabel beban kerja yang dominan adalah Central Demand (CD) sebesar 33,63%, dengan sebaran beban kerja mental sebesar 55,10% dan beban kerja fisik sebesar 44,90%. Dengan demikian, beban kerja mental yang dirasakan oleh Supervisory Level masih relatif sebanding dibandingkan beban kerja fisiknya, dan berbeda yang dirasakan oleh Managerial Level dimana lebih didominasi oleh beban kerja mental.

Objective: A study was run to test the sensitivity of multiple workload indices to the differing cognitive demands of four military monitoring task scenarios and to investigate relationships between indices.
Background: Various psychophysiological indices of mental workload exhibit sensitivity to task factors. However, the psychometric properties of multiple indices, including the extent to which they intercorrelate, have not been adequately investigated.
Method: One hundred fifty participants performed in four task scenarios based on a simulation of unmanned ground vehicle operation. Scenarios required threat detection and/or change detection. Both single- and dual-task scenarios were used. Workload metrics for each scenario were derived from the electroencephalogram (EEG), electrocardiogram, transcranial Doppler sonography, functional near infrared, and eye tracking. Subjective workload was also assessed.
Results: Several metrics showed sensitivity to the differing demands of the four scenarios. Eye fixation duration and the Task Load Index metric derived from EEG were diagnostic of single-versus dual-task performance. Several other metrics differentiated the two single tasks but were less effective in differentiating single- from dual-task performance. Psychometric analyses confirmed the reliability of individual metrics but failed to identify any general workload factor. An analysis of difference scores between low- and high-workload conditions suggested an effort factor defined by heart rate variability and frontal cortex oxygenation.
Conclusions: General workload is not well defined psychometrically, although various individual metrics may satisfy conventional criteria for workload assessment.
Application: Practitioners should exercise caution in using multiple metrics that may not correspond well, especially at the level of the individual operator.

The goal of this study is to analyze the mediating role of workload on the relationship between leader member exchange (LMX) and job satisfaction. Another objective of this study is to examine the relationship between LMX, workload, job satisfaction and demographic variables. Additionally, the differences of LMX, workload and job satisfaction according to demographic variables are investigated. The major aim is to reflect the issues that lead to job dissatisfaction. It is indicated that LMX and workload can be two of the most fundamental determinants for understanding this issue. In order to clarify this assumption, the research was conducted with one of the largest municipality in Istanbul, Turkey, with 255 participants. The result of this research suggests that workload is a partial mediator in the relationship between LMX and job satisfaction. Meanwhile, no differences were found in LMX, job satisfaction or workload according to gender, education and status, but were found according to age, job seniority and organizational seniority.

Workload capacity describes processing efficiency across changes in workload, and is an important concept in many areas of psychology. The capacity coefficient is one particularly useful measure of this construct, which is a function across time. Until now most analyses of the capacity coefficient focused on the overall height of this function, often only in terms of a qualitative judgment (greater than or less than one). In this work we detail how a functional extension of principal components analysis can enable us to capture the time extended information of this functional data and yet still describe differences across participants or conditions using a small number of scalar values chosen to maximally distinguish among as set of capture coefficients. The approach opens up many possibilities for a more fine grained study of differences in workload capacity across tasks as well as across individuals.

An accurate measure of mental workload in human operators is a critical element of monitoring and adaptive aiding systems that are designed to improve the efficiency and safety of human-machine systems during critical tasks. Functional near infrared (fNIR) spectroscopy is a field-deployable non-invasive optical brain monitoring technology that provides a measure of cerebral hemodynamics within the prefrontal cortex in response to sensory, motor, or cognitive activation. In this paper, we provide evidence from two studies that fNIR can be used in ecologically valid environments to assess the: 1) mental workload of operators performing standardized (n-back) and complex cognitive tasks (air traffic control--ATC), and 2) development of expertise during practice of complex cognitive and visuomotor tasks (piloting unmanned air vehicles--UAV). Results indicate that fNIR measures are sensitive to mental task load and practice level, and provide evidence of the fNIR deployment in the field f...

Functional near-infrared spectroscopy (fNIR) is a highly portable, safe neuroimaging technology that uses light to measure cortical brain activity. We have utilized fNIR to provide objective measures of cognitive workload of certified Air Traffic Controllers while they managed realistic scenarios under typical and emergent conditions. Participants also completed an n-back task, which is a standardized working memory and attention task with four incremental levels of difficulty. In the n-back results, as task difficulty increases, accuracy and speed of the participants decrease monotonically. Blood oxygenation changes, as measured by fNIR, monotonically increased with increasing task difficulty. These findings are in line with earlier studies. Further, fNIR measures were analyzed for comparison between data-based communication and voice-based communication during an air Ayaz, H.

An accurate measure of mental workload in human operators is a critical element of monitoring and adaptive aiding systems that are designed to improve the efficiency and safety of human-machine systems during critical tasks. Functional near infrared (fNIR) spectroscopy is a field-deployable non-invasive optical brain monitoring technology that provides a measure of cerebral hemodynamics within the prefrontal cortex in response to sensory, motor, or cognitive activation. In this paper, we provide evidence from two studies that fNIR can be used in ecologically valid environments to assess the: 1) mental workload of operators performing standardized (n-back) and complex cognitive tasks (air traffic control -ATC), and 2) development of expertise during practice of complex cognitive and visuomotor tasks (piloting unmanned air vehicles -UAV). Results indicate that fNIR measures are sensitive to mental task load and practice level, and provide evidence of the fNIR deployment in the field for its ability to monitor hemodynamic changes that are associated with relative cognitive workload changes of operators. The methods reported here provide guidance for the development of strategic requirements necessary for the design of complex human-machine interface systems and assist with assessments of human operator performance criteria.

Two groups, aged 25-35 and 60-69, engaged in 3 levels of a delayed auditory recall task while driving a simulated highway. Heart rate and skin conductance increased with each level of demand, demonstrating that these indices can correctly rank order cognitive workload. Effects were also observed on speed and SD of lane position, but they were subtle, nonlinear, and did not effectively differentiate. Patterns were quite consistent across age groups. These findings on the sensitivity of physiological measures replicate those from an onroad study using a similar protocol. Together, the results support the validity of using these physiological measures of workload in a simulated environment to model differences likely to be present under actual driving conditions.

The purpose of this study was to identify the influence of situation complexity and driving experience on subjective workload and driving performance, and the less costly and the most effective strategies faced with a hazard pedestrian crossing. Four groups of young drivers (15 traditionally trained novices, 12 early-trained novices, 15 with three years of experience and 15 with a minimum of five years of experience) were randomly assigned to three situations (simple, moderately complex and very complex) including unexpected pedestrian crossings, in a driving simulator. The subjective workload was collected by the NASA-TLX questionnaire after each situation. The main results confirmed that the situation complexity and the lack of experience increased the subjective workload. Moreover, the subjective workload, the avoidance strategies and the reaction times influenced the number of collisions depending on situation complexity and driving experience. These results must be taken into account to target the prevention actions.

This paper suggests an algorithm for estimating driver's cognitive workload complexity using driving performance and physiological data. The algorithm adopts radial basis probabilistic neural networks (RBPNN) to construct estimation models. In this study, combinations of two driving performance data including standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), and two physiological signals including heart rate (HR) and skin conductance level (SCL) were considered as measures of cognitive workload. Data for training and testing the RBPNN models were collected in a driving simulator in which fifteen participants drove through a highway and were asked to complete auditory recall tasks which consist of three levels of difficulty. The best performing model, which uses SDLP and SCL data over a 20s-window, could identify four graded levels of cognitive workload with an average accuracy of 85.6%. The results demonstrated that the model using SDLP and SCL was outperforming than the other combinations among performance and physiological measures.

With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model implemented included an Adaptive Neuro Fuzzy Inference System (ANFIS), which was used in different configurations to fuse data from an Electroencephalogram (EEG) model’s output, four eye activity features and a control input feature. The online validation of the ANFIS models produced good results, while the best performing model (containing all four eye activity features and the control input feature) showed an average Mean Absolute Error (MAE) = 0.67 ± 0.18 and Correlation Coefficient (CC) = 0.71 ± 0.15. The remaining six ANFIS models included data from the EEG model’s output, which had an offset discrepancy. This resulted in an equivalent offset for the online multimodal fusion. Nonetheless, the efficacy of these ANFIS models could be confirmed by the pairwise correlation with the task level, where one model demonstrated a CC = 0.77 ± 0.06, which was the highest among all of the ANFIS models tested. Hence, this study demonstrates the suitability for online multimodal fusion of features extracted from EEG signals, eye activity and control inputs to produce an accurate and repeatable inference of MWL.