Continuous Workload Research Articles

Machine learning-based multimodal fusion recognition of passenger ship seafarers’ workload: A case study of a real navigation experiment

Passenger ships have complex transportation systems and seafarers face high workloads, making them susceptible to serious injuries and fatalities in the event of accidents. Existing unimodal workload recognition for seafarers mainly focuses on fixed load induction in bridge simulators, whereas a multimodal approach using multi-sensor data fusion can overcome the reliability and sensitivity limitations of a single sensor. To accurately identify the workload of seafarers, we propose a machine learning-based multimodal fusion method at the feature layer and utilise the Gini index to determine the feature weight of the multimodal data. Through a real ship navigation experiment, the subjective workload assessment technique (SWAT) was employed to collect the continuous workload scores of 24 seafarers in daily tasks. Further, the Dempster–Shafer evidence theory was used to integrate these scores with the unsafe behavior probability of seafarers to obtain a calibrated workload. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals were collected in real time, and a high-dimensional feature matrix was extracted to construct the workload recognition model. Random forest, XGBoost, and back-propagation neural networks were used to establish multimodal fusion workload recognition models at the feature-fusion stage, and the model performances were compared. The results showed that the multimodal fusion based on EEG, ECG, and EDA had an excellent recognition accuracy. The XGBoost algorithm has better performance with an accuracy of 85.72%, which is an increment of 9.49% compared to that of the unimodal algorithm, and this improvement passed the statistical significance test. Important features suitable for multimodal fusion recognition were also analysed. The findings of this study have profound significance for enhancing risk control measures pertaining to human factors and ensuring navigation safety.

Predictive Mental Workload Modeling Methodology for Single-Pilot Operations System Design

Predictive mental workload model is an important tool for evaluating early single-pilot operations (SPO) system design and can be used to explore the impact of different function allocation decisions before prototype or simulator implementation. To build this model, this paper proposes a methodology based on task analysis. Firstly, it selects the approach and landing scenario of the current two-pilot operations (TCO) and completes the task description of the TCO using the hierarchical task analysis. Secondly, it identifies the function allocation requirements for the transition from TCO to SPO using the work domain analysis and proposes the original function allocation decisions based on the SPO’s system framework. Finally, it creates the predictive mental workload model for evaluating different SPO system design options based on temporal network and multiple resource theory. The researchers use the created model to evaluate the original function allocation decisions and find that the designed SPO system has instantaneous workload values that exceed the upper limit at multiple time points. By analyzing the causes of these problems, the researchers introduce audible warnings and visual indicators to the SPO system to improve design decisions and evaluate these decisions. The results show that the continuous workload profile and overall average workload of the redesigned SPO system are better than the current TCO system, and also prove that the predictive mental workload model can be well applied to evaluate the system design of early SPO.

Venous volume and compliance in the calf and forearm does not change after acute endurance exercise performed at continuous or interval workloads.

Short‐term endurance exercise training for 6–8 weeks leads to increases in venous volume and compliance in the limbs. However, it is not known whether these venous vascular properties are improved by acute endurance exercise. We examined the effects of acute endurance exercise involving continuous or interval workloads on venous volume and compliance in the exercising (calf) and non‐exercising (forearm) limbs. Sixteen healthy young volunteers performed cycling exercise involving a continuous workload of 60% heart rate (HR) reserve or an interval workload of 40% HRreserve and 80% HRreserve, alternating every 2 min, for a total of 32 min each. Before and 60 min after acute cycling exercise, venous volume in the calf and forearm was measured by venous occlusion plethysmography during a cuff‐deflation protocol with a venous collecting cuff wrapped to the thigh and upper arm and strain gauges attached to the calf and forearm. The cuff pressure was maintained at 60 mmHg for 8 min and was then deflated to 0 mmHg at a rate of 1 mmHg/s. Venous compliance was calculated as the numerical derivative of the cuff pressure–limb venous volume curve. In both the calf and forearm, the cuff pressure–venous volume curve and the cuff pressure–venous compliance relationship did not differ between before and 60 min after exercise involving continuous or interval workloads. These results suggest that acute exercise does not improve venous volume and compliance in both the exercising and non‐exercising limbs.

DISTRIBUTIONS OF AIR TRAFFIC CONTROL STUDENTS’ ATTITUDES TOWARDS WORKLOAD

Mental workload is a well-known concept with a long development history. It can be used to examine students’ attitudes at the end of the educational process and compare them in groups or separately. However, building a continuous workload profile across the range of task complexity increase is still an urgent issue. All four groups of methods used to define mental workload have such flaws for the workload profile construction process as significant time requirements, single value processing and post-processing of the received results. Only one of them can be used without modifications to construct the operator’s attitude chart (profile) regarding the workload range and it doesn’t operate with more reliable absolute values. To resolve this problem, a special workload assessment grid was developed, considering the advantages of a subjective group of methods and seven core characteristics. The reasoning for grid axes choice, threshold values, and question formulation were provided. Statistics were calculated for the full sample, different grades, and educational institutions. Comparison of the received responses with referential values, cross-comparison between institutions and different grades were performed. The results contribute to such important aspects of workload, as redlines, workload profiling, and operator’s comparison.

Scheduling multiple scientific workflows using containers on IaaS cloud

With the adaptable paradigm of cloud computing and obtainable of data accumulated from largely high-powered scientific devices, workflows have turn into an occurring aim to execute considerable scientific advances at an enhanced speed. Occurring Workflow as a Service (WaaS) frameworks provide scientists an effortless, simply accessible and cost-efficient manner of using their applications from anywhere and at anytime in the cloud. They are multitenant platforms and are developed to handle the execution of heterogeneous workflows continuous workload. To fulfill this, they utilize the compute, network and storage services provided by Infrastructure as a Service (IaaS) vendors. Therefore, at any considerable particular moment, a WaaS framework should be proficient of effectively schedule these continuous workload of workflows with various features and quality of service (QoS). Therefore, we propose a strategy of scheduling and resource provisioning planned particularly for WaaS platforms. The algorithm is dynamic and scalable to adjust to improve in the workload and platform. It supports containers to deal the inefficiency of resource utilization and targets to reduce the overall execution cost of infrastructure resources as fulfilling each single workflow deadline constraint. To our information, this approach that explicitly deals VM sharing in the subject of WaaS by devising the utilization of containers in the heuristics of scheduling and resource provisioning. Our experimental results shows its responsiveness to the uncertainties of the environment, its potential to achieve deadlines, and its cost-effectiveness when compared to other recent algorithms.

Dynamic Consolidation of Virtual Machine: A Survey of Challenges for Resource Optimization in Cloud Computing

Background:: Virtualization is an efficient technology that accelerates available data center to support efficient workload for the application. It completely based on guest operating system which keeps track of infrastructure that keeps track of real time usage of hardware and utilization of software. Objective:: To address the issues with Virtualization this paper analyzed various virtualization terminology for treating best effective way to reduce IT expenses while boosting efficiency and deployment for all levels of businesses. Methods: This paper discusses about the scenarios where various challenges met by Dynamic VM consolidation. Dynamic conclusion of virtual machines has the ability to increase the consumption of physical setup and focus on reducing power utilization with VM movement for stipulated period. Gathering the needs of all VM working in the application, adjusting the Virtual machine and suitably fit the virtual resource on a physical machine. Profiling and scheduling the virtual CPU to another Physical resource. This can be increased by making live migration with regards to planned schedule of virtual machine allotment. Results:: The recent trends followed in comprehending dynamic VM consolidation is applicable either in heuristic-based techniques which has further approaches based on static as well as adaptive utilization threshold. SLA with unit of time with variant HOST adoption (SLATAH) which is dependent on CPU utilization threshold with 100% for active host. Conclusion:: The cloud provider decision upon choosing the virtual machine for their application also varies with their decision support system that considers data storage and other parameters. It is being compared for the continuous workload distribution as well as eventually compared with changing demands of computation and in various optimization VM placement strategies.

P6372Does modification of activity regimes optimize the effect of high physical activity on hypertensive heart disease?

Abstract Aims and background Spontaneously hypertensive rats (SHR) are a suitable model of essential hypertension and allow analyze the progression of hypertension and hypertension-dependent end-organ damage. In this model, acute improvement of physical activity (free running wheel activity) exerts known beneficial effects (such as lowering oxidative stress). However, these initial beneficial effects are lost during the continuation of high physical activity and translate into mal-adaptive processes, suggesting that not any high physical activity exerts beneficial effects. It has been hypothesized that the skeletal muscle release myokines, that contribute to the beneficial effects of exercise. However, myokines, such as IL-6, are induced by an acute increase in work load not by continuous work load. Therefore, we analyzed whether modification of high physical activity, i.e. intermittent free running wheel activity, modify the long-term impact on long lasting hypertension. Methods 38 female SHR aged 6 weeks (pre-hypertensive state) were randomly allocated to one of the following groups: Sedentary (S; standard holding condition) imitating the condition of sedentary life style, high activity (HA; life-long free running wheel) imitating the condition of active life style, temporary activity (TA; 6 months free running wheel and 3 months sedentary) imitating the loss of active life style during ageing, and finally intermittent activity (IA; 10 months repetitive access to running wheels every 4 weeks) imitating altered workloads. All rats were sacrificed at the age of 10 months. Results IA was the only treatment regime that effectively lowered blood pressure (P syst: 186±11 vs. 165±6 mmHg), improved ejection fraction (EF: 56±5 vs. 63±2%), and displayed clear molecular profile of adaptive myocardial hypertrophy rather than mal-adaptive hypertrophy. Moreover, only IA reduced the number of circulating monocytes (377±102 vs. 220±16 /μl), a cell population that immigrated in the left ventricle. The number of monocytes was directly correlated with the expression of MMP12, BNP, ANP, biglycan, collagen-1, actinin, β-MHC, and somatstatin but inversely related to β-adrenoceptor, Glut-4, and UCP3 expression. Finally, IA increased the skeletal expression of IL-6 and decreased the renal expression of AT1 receptors. Conclusion The data confirm the previous findings that not all type of physical activity beneficially affects hypertensive-dependent disease. In contrast, the data support the hypothesis that alterations in work load are required triggering the release of myokines from the skeletal muscle and identify the amount of circulating monocytes as a main trigger of mal-adaptive hypertrophy in these rats. The data are important with respect to optimize life style suggestions for patients with essential hypertension. Acknowledgement/Funding DFG (ERAGON and SFB 1213)

Effects of Acute Normobaric Hypoxia on Non-linear Dynamics of Cardiac Autonomic Activity During Constant Workload Cycling Exercise.

AimMeasurements of Non-linear dynamics of heart rate variability (HRV) provide new possibilities to monitor cardiac autonomic activity during exercise under different environmental conditions. Using detrended fluctuation analysis (DFA) technique to assess correlation properties of heart rate (HR) dynamics, the present study examines the influence of normobaric hypoxic conditions (HC) in comparison to normoxic conditions (NC) during a constant workload exercise.Materials and MethodsNine well trained cyclists performed a continuous workload exercise on a cycle ergometer with an intensity corresponding to the individual anaerobic threshold until voluntary exhaustion under both NC and HC (15% O2). The individual exercise duration was normalized to 10% sections (10–100%). During exercise HR and RR-intervals were continuously-recorded. Besides HRV time-domain measurements (meanRR, SDNN), fractal correlation properties using short-term scaling exponent alpha1 of DFA were calculated. Additionally, blood lactate (La), oxygen saturation of the blood (SpO2), and rating of perceived exertion (RPE) were recorded in regular time intervals.ResultsWe observed significant changes under NC and HC for all parameters from the beginning to the end of the exercise (10% vs. 100%) except for SpO2 and SDNN during NC: increases for HR, La, and RPE in both conditions; decreases for SpO2 and SDNN during HC, meanRR and DFA-alpha1 during both conditions. Under HC HR (40–70%), La (10–90%), and RPE (50–90%) were significantly-higher, SpO2 (10–100%), meanRR (40–70%), and DFA-alpha1 (20–60%) were significantly-lower than under NC.ConclusionUnder both conditions, prolonged exercise until voluntary exhaustion provokes a lower total variability combined with a reduction in the amplitude and correlation properties of RR fluctuations which may be attributed to increased organismic demands. Additionally, HC provoked higher demands and loss of correlation properties at an earlier stage during the exercise regime, implying an accelerated alteration of cardiac autonomic regulation.

Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks.

One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.

Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Emerging Workflow as a Service (WaaS) platforms offer scientists a simple, easily accessible, and cost-effective way of deploying their applications in the cloud at anytime and from anywhere. They are multi-tenant frameworks and are designed to manage the execution of a continuous workload of heterogeneous workflows. To achieve this, they leverage the compute, storage, and network resources offered by Infrastructure as a Service (IaaS) providers. Hence, at any given point in time, a WaaS platform should be capable of efficiently scheduling an arbitrarily large number of workflows with different characteristics and quality of service requirements. As a result, we propose a resource provisioning and scheduling strategy designed specifically for WaaS environments. The algorithm is scalable and dynamic to adapt to changes in the environment and workload. It leverages containers to address resource utilization inefficiencies and aims to minimize the overall cost of leasing the infrastructure resources while meeting the deadline constraint of each individual workflow. To the best of our knowledge, this is the first approach that explicitly addresses VM sharing in the context of WaaS by modeling the use of containers in the resource provisioning and scheduling heuristics. Our simulation results demonstrate its responsiveness to environmental uncertainties, its ability to meet deadlines, and its cost-efficiency when compared to a state-of-the-art algorithm.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

Virtual networks dependability assessment framework

Advances in virtualisation technology have enabled the development of network virtualisation that complements server virtualisation by enabling continuous workload agility irrespective of the network addressing and protocol of the underlying physical network. Despite huge benefits both in cost and accessibility, network virtualisation is susceptible to failure due to a wide variety of factors. Therefore, dependability in a virtualised network environment is a significant issue that need to be addressed before the full benefits of network virtualisation can be exploited. In this paper, we propose a framework to estimate dependability risks in virtual network environments considering variations in the virtual network configurations. The proposed framework uses the reliability block diagram (RBD) and continuous time Markov chains (CTMC) to model and analyse the dependability level of a virtual network. The proposed framework is helpful to the design and construction of more dependable VNE.

A hybrid cloud controller for vertical memory elasticity: A control-theoretic approach

Web-facing applications are expected to provide certain performance guarantees despite dynamic and continuous workload changes. As a result, application owners are using cloud computing as it offers the ability to dynamically provision computing resources (e.g., memory, CPU) in response to changes in workload demands to meet performance targets and eliminates upfront costs. Horizontal, vertical, and the combination of the two are the possible dimensions that cloud application can be scaled in terms of the allocated resources. In vertical elasticity as the focus of this work, the size of virtual machines (VMs) can be adjusted in terms of allocated computing resources according to the runtime workload. A commonly used vertical resource elasticity approach is realized by deciding based on resource utilization, named capacity-based. While a new trend is to use the application performance as a decision making criterion, and such an approach is named performance-based. This paper discusses these two approaches and proposes a novel hybrid elasticity approach that takes into account both the application performance and the resource utilization to leverage the benefits of both approaches. The proposed approach is used in realizing vertical elasticity of memory (named as vertical memory elasticity), where the allocated memory of the VM is auto-scaled at runtime. To this aim, we use control theory to synthesize a feedback controller that meets the application performance constraints by auto-scaling the allocated memory, i.e., applying vertical memory elasticity. Different from the existing vertical resource elasticity approaches, the novelty of our work lies in utilizing both the memory utilization and application response time as decision making criteria. To verify the resource efficiency and the ability of the controller in handling unexpected workloads, we have implemented the controller on top of the Xen hypervisor and performed a series of experiments using the RUBBoS interactive benchmark application, under synthetic and real workloads including Wikipedia and FIFA. The results reveal that the hybrid controller meets the application performance target with better performance stability (i.e., lower standard deviation of response time), while achieving a high memory utilization (close to 83%), and allocating less memory compared to all other baseline controllers.

THE EFFECTS OF TECHNOLOGY BASED CONTROL SYSTEMS ON WORKPLACE BEHAVIOR OF THE WORKERS IN LOGISTICS SECTOR

Logistics industry is of particular importance for companies by means of serving their customers at correct time, price and amounts. The quality of service is targeted to be increased by the investments made to the technological developments which affect the performance of the employee in the logistics industry. The developments experienced at information and communication technologies provide competitive advantage to the businesses. However, they can also cause negative results over the performance of employees due to the stress caused by continuous tracking and work load. Within this scope technology-based control systems which control the drivers at work in the logistics industry examined initially in this study. Later, the effects of systems on drivers have been determined by survey method. For his purpose, 547 questionnaires were obtained from 168 logistics companies operating in Turkey. The data obtained analyzed by using SPSS 15 statistical software.By the analysis of data obtained from the surveys, it is determined that the technology-based control systems have effects on the behaviors of employee at work.

Continuous workload control order release revisited: an assessment by simulation

Order release is a key component of the Workload Control concept. Jobs do not enter the shop floor directly – they are retained in a pre-shop pool and released in time to meet due dates while keeping work-in-process within limits or norms. There are two important groups of release methods: continuous methods, for which the workload falling to a specified level can trigger a release at any moment in time; and, periodic release methods, for which releases take place at fixed intervals. Continuous release methods in general have been shown to outperform periodic release methods. Yet, there is incongruence in the results presented in the literature on the relative performance of the various continuous release methods. We use a job shop simulation model to examine the performance of continuous release methods from the literature and find that the contradictory results are explained by the different rules applied to sequence jobs in the pool – a factor neglected in previous work. Finally, a new breed of continuous release methods has recently emerged, but these have not been compared with prior approaches. Therefore, we also examine these methods and show that they significantly improve overall performance, although this is to the detriment of jobs with large processing times.

Latent cytomegalovirus infection and innate immune function following a 75 km cycling time trial

This study compared the acute immune response, inflammation, and lipid peroxidation to a 75km cycling time trial in male athletes testing positive or negative for latent cytomegalovirus (CMV) infection. Trained cyclists (N=20) were tested for CMV serostatus, and cycled 75km on a mountainous course using indoor trainers with continuous workload monitoring. Pre-, post-, and 1h post-exercise blood samples were analyzed for total blood leukocyte counts, blood granulocyte (GR) and monocyte (MO) phagocytosis (PHAG) and oxidative burst activity (OBA), four plasma cytokines, and plasma F2-isoprostanes. Forty percent of the subjects tested positive for CMV. No differences in subject characteristics were found between CMVpos and CMVneg groups. Mean power (57.3±1.6, 59.4±1.8% maximal Watts, p=0.803), heart rate (87.0±1.0, 86.5±1.3% maximal heart rate, p=0.376), and total time (2.56±0.08, 2.60±0.08h, p=0.744) to complete the 75km cycling time trial did not differ between CMVpos and CMVneg groups. Whereas exercise induced significant changes in total blood leukocyte counts, GR and MO-PHAG, four plasma cytokines, and plasma F2-isoprostanes (p<0.05, ω(2)>0.03), these exercise-induced changes did not differ between CMVpos and CMVneg groups (p>0.05, ω(2)<0.01). CMV serostatus does not appear to influence these innate immune responses or markers of inflammation and lipid peroxidation in response to a single bout of heavy exertion.

Fuzzy uncertainty modelling for project planning: application to helicopter maintenance

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance.

Methodology of measurement, documentation and programming of optimal continuous workload with variable intensity - applications in sports medicine, physiotherapy, geriatrics, health-related training and sport for all

Background Continuous effort can be defined as the activity of every muscle since the impulses of any intended movement aiming at implementation of a given goal result in an increase of heart muscle contractions beyond resting heart rate until the end of a given activity. The goal of this paper is the methodology of measurement and documentation of continuous physical effort with variable intensity. Material/Methods: Methods used in the study included the Delphi method, analysis of the literature and scientific documentation, participant observation, case study and designing elements. The analysis of over 3000 reports of researches performed by students of physiotherapy and recreation (n=855) during standard health-related training including those based on the exercises of safe falling and avoiding collisions (participant observation) was conducted in 2008-2012. Results: The considerable advantage of the methodology of measurement, documentation and programming of the workload presented herein is the possibility to express this variable with the use of universal index, referred to as LE and based on conventional units. The index can be used at any level of preliminary analysis i.e. from particular exercises and set of exercises, training sessions to microcycles, mesocycles, macrocycles and long-term cycles. Conclusions: The reduction of health promotion and physical activity mainly to walking, running, cycling or other simple forms of exercise may counterproductive in the longer term. That is, instead of achieving the ultimate goal of common physical activity we will achieve its negation.

Evaluation Of Cognitive Workload From EEG During A Mental Arithmetic Task

We collected electroencephalographic (EEG) data from 16 subjects while they performed a mental arithmetic task at five different levels of difficulty. A classifier was trained to discriminate between three conditions: relaxed, low workload and high workload, using spectral features of the EEG. We obtained an average classification accuracy of 62%. A continuous workload index was obtained by low-pass filtering the classifier’s output. The correlation coefficient between the resulting workload index and the difficulty level of the task was 0.6 on average.

A method for building a real-time cluster-based continuous mental workload scale

Mental workload is known to significantly influence an operator's task performance. Extreme levels of mental workload can lead to operator monotony, low performance and operation errors. Measurement of mental workload is thus very important–especially real-time measurement that involves the representation of continuous real numbers. Existing mental workload measures include human subjects’ self-reporting, task performance and psychophysiological signals. Subjects’ self-reporting and task performance measures are used posterior–i.e. after the task is performed–and thus they cannot be used for real-time measurement of mental workload. Measures based on psychophysiological signals are suitable for real-time measurement and currently are usually represented as a set of discrete numbers–so-called levels. Mental workload is essentially a quantity of continuous numbers and defining it as a set of discrete levels can introduce unnecessary constraints on the accurate understanding of mental workload. In this paper, a novel methodology is proposed for constructing a measure of mental workload with a continuous real number representation. This methodology is based on the view that mental workload is task dependent and its quantitative representation of high or low mental workload should be dependent on training data. For the purpose of illustrating and validating this methodology, the electrocardiogram (ECG) signal was used. The proposed ECG-based measure was compared with the Rating Scale Mental Effort (RSME) method in a driving application. The result of the validation has shown that the proposed method is in good agreement with the RSME method; however, it is known that RSME cannot be done in real time and with a representation of a set of levels.