Different Levels Of Workload Research Articles

Using the ISO Detection response task to measure the cognitive load of driving four separate vehicles on two distinct highways

The ISO Detection Response Task (DRT) is a standard tool for assessing drivers’ cognitive load and it has primarily been used to measure the cognitive load of completing non-driving tasks and interacting with vehicle systems. In this study we use the DRT to measure the workload of driving four separate vehicles (a 2019 Tesla Model 3, a 2018 Cadillac CT6, a 2018 Volvo XC90, a 2019 Nissan Rogue) in manual mode and on two distinct roadways (US Interstate Highway 15 and 80) in and around Salt Lake City, UT. Results showed that the unique road characteristics of I-80 resulted in higher levels of cognitive load as demonstrated by the slower DRT response times. Likewise, different levels of workload were found across the four vehicles, with higher workload levels found for one of the four vehicles. This study expands the use of the DRT outside its original area of application, and advances it as a tool to assess the cognitive demand induced by varying road and vehicle characteristics.

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.

Analysis of the Relationships between Noise Exposure and Stress/Arousal Mood at Different Levels of Workload

Analysis of the Relationships between Noise Exposure and Stress/Arousal Mood at Different Levels of Workload

How Demanding Is Healthcare Work? A Meta-Analytic Review of TLX Scores.

This study establishes how demanding healthcare work is experienced to be and whether nurses and physicians experience different levels of workload. A meta-analytic review was conducted of 87 studies that reported Task Load Index (TLX) scores for healthcare work. Of these studies, 37 were conducted in real-life settings and 50 in lab settings without real patients. In real-life settings, clinicians experienced a workload with a mean TLX of 49 (on a 0-100 scale). Divided onto staff groups, the mean TLX for nurses was 63, which was significantly higher than the mean of 40 for physicians. Among the six TLX subscales, the main contributors to workload were mental demand, temporal demand, and effort. They were higher than physical demand and frustration. The clinicians experienced their performance - the last subscale - as closer to poor than good in 38% of the studies conducted in real-life settings. The difference between nurses and physicians was consistent across all subscales, except mental demand. Finally, it is methodologically important that TLX scores appeared not to transfer directly from lab to real-life settings. To reduce the risk of errors and burnout, new healthcare procedures and technologies should be evaluated for their impact on workload.

Evaluating Levels of Automation in Human–Robot Collaboration at Different Workload Levels

This study explored how levels of automation (LOA) influence human robot collaboration when operating at different levels of workload. Two LOA modes were designed, implemented, and evaluated in an experimental collaborative assembly task setup for four levels of workload composed of a secondary task and task complexity. A user study conducted involving 80 participants was assessed through two constructs especially designed for the evaluation (quality of task execution and usability) and user preferences regarding the LOA modes. Results revealed that the quality of task execution and usability was better at high LOA for low workload. Most of participants also preferred high LOA when the workload increases. However, when complexity existed within the workload, most of the participants preferred the low LOA. The results reveal the benefits of high and low LOA in different workload situations. This study provides insights related to shared control designs and reveals the importance of considering different levels of workload as influenced by secondary tasks and task complexity when designing LOA in human–robot collaborations.

Influence of workload and weather conditions on rolling behaviour of horses and mules

We evaluated the influences of workload intensity, bath handling and environmental conditions in the rolling behaviour of horses and mules. For this purpose, animals were observed after being exposed to different levels of workload and the rolling behaviour was recorded and described. During all testing procedures, the weather condition (temperature and humidity) was registered by a Black globe and Wet Bulb apparatus. Horses frequently rolled after intense exercise and after bath handling, independently of the weather condition. Mules frequently rolled after control (no exercise) and intense exercise in warmer and wetter days and after bath handlings in colder and drier days. Rolling behaviour characteristics were slightly different between species. While mules exhibited more frequently the behaviours of rest, self-care, yawn, tail swishing, complete spins, snort vocalization and use of the same spot to roll, horses exhibited more frequently the behaviours of paw, sniff, head and neck rubbing movements and incomplete spins to the right and left. This findings evidence that rolling is an important and frequently exhibited behaviour by domestic horses and mules, but animals may express it differently and may have their own motivations. In this sense, we believe that domestic equines, mainly those raised in more intensive livestock, should have the opportunities to freely express the rolling, as a way to improve their welfare conditions and with agreement with a more rational handling practice.

EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other

Objective. A passive brain-computer interface (pBCI) is a system that continuously adapts human-computer interaction to the user’s state. Key to the efficacy of such a system is the reliable estimation of the user’s state via neural signals, acquired through non-invasive methods like electroencephalography (EEG) or near-infrared spectroscopy (fNIRS). Many studies to date have explored the detection of mental workload in particular, usually for the purpose of improving safety in high risk work environments. In these studies, mental workload is generally modulated through the manipulation of task difficulty, and no other aspect of the user’s state is taken into account. In real-life scenarios, however, different aspects of the user’s state are likely to be changing simultaneously—for example, their cognitive state (e.g. level of mental workload) and affective state (e.g. level of stress/anxiety). This inevitable confounding of different states needs to be accounted for in the development of state detection algorithms in order for them to remain effective when taken outside the lab. Approach. In this study we focussed on two different states that are of particular importance in high risk work environments, specifically mental workload and stress, and explored the effect of each on the ability to detect the other using EEG signals. We developed an experimental protocol in which participants performed a cognitive task under two different levels of workload (low workload and high workload) and at two levels of stress (relaxed and stressed) and then used a linear discriminant classifier to perform classification of workload level and stress level independently. Main results. We found that the detection of both mental workload level (e.g. low workload vs. high workload) and stress level (e.g. stressed vs. relaxed) were significantly diminished if the training and test data came from different as opposed to the same level of the other state (e.g. for mental workload classification, training on data from a relaxed condition and testing on data from a stressed condition, rather than both training and testing on the relaxed condition). The reduction in classification accuracy observed was as much as 15%. Significance. The results of this study indicate the importance of considering multiple aspects of a user’s state when developing detection algorithms for pBCI technologies.

Physical and mental workload interactions in a sterile processing department

Work in healthcare facilities has been categorized as a high-risk job for developing Work-Related Musculoskeletal Disorders (WMSDs). Little attention has been given to the Sterile Processing Department (SPD) employees who are exposed to both mentally and physically demanding conditions, factors that may impose an elevated risk in the development of WMSDs. Previous studies have shown that WMSDs can be a result of complex interactions between physical, psychosocial, biological, and individual characteristics. However, the evidence of specific associations is still inconclusive. The objective of this study was to determine if the perception of mental workload causes workers to adopt more risky body postures. The study was carried out in four phases: 1) identification of musculoskeletal disorders using customized Nordic questionnaires, 2) ergonomic assessment of SPD using REBA method, 3) mental workload assessment of SPD using NASA-TLX, and 4) interaction between physical (postural risk) and mental workload through the performance of a task at two different levels of workload conducted through a controlled experiment. The findings indicate that increases in REBA scores and NASA-TLX indices were associated with days and conditions with higher workloads. Results showed evidence that increments on mental workload are related to worsening postures, increasing the risk of developing musculoskeletal disorders. These results serve to raise awareness and warn employees about the need to pause and analyze the way they perform their duties under high levels of workload in an attempt to reduce WMSDs risk in healthcare sector employees.

Workload assessment of air traffic controllers

Air traffic controllers (ATCOs) are exposed to high workload even during routine operations. Their job carries a lot of responsibility and consists of complex tasks. In order to enhance high safety standards, a systematic evaluation of workload levels is required. This study explores an objective measurement of workload. Heart activity of ATCOs was monitored in order to indicate psychological status of ATCOs while being exposed to different levels of workload - in terms of changing traffic loads and airspace complexity. A standard method was used for the evaluation of heart activity. This method includes the frequency analysis in context of heart rate variability; time intervals of consecutive R waves in the heart activity record are assessed. The evaluation of the ratio of power in the low-frequency and high-frequency bands (indicator of sympathovagal balance) as well as parameters derived from fluctuation analysis were used. The results indicated that parameters of heart rate variability based on spectral analysis were significant in case of workload evaluation.

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians.

BackgroundIt has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician.MethodsElectrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics’ ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics).ResultsThe non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680).ConclusionNon-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician’s workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians’ mean HR was not a valid marker of workload.

Personalized Driver Workload Inference by Learning From Vehicle Related Measurements

Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers’ workload can enhance both safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in an appropriate manner. Rather than developing an average model for all drivers, a personalized driver workload inference (PDWI) system considering individual drivers driving characteristics is developed using machine learning techniques via easily accessed vehicle related measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers workload is first automatically splitted into different categories according to its inherent data characteristics using fuzzy C-means (FCM) clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload type can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, ${F_{1}}$ -score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.

Workload differences across command levels and emergency response organizations during a major joint training exercise.

This study reports on an initial test using a validated workload measurement method, the NASA Task Load Index (TLX), as an indicator of joint emergency exercise effectiveness. Prior research on emergency exercises indicates that exercises must be challenging, ie, result in high workload, to be effective. However, this is often problematic with some participants being underloaded and some overloaded. The NASA TLX was used to test for differences in workload between commanders and subordinates and among three different emergency response organizations during a joint emergency exercise. Questionnaire-based evaluation with professional emergency responders. The study was performed in conjunction with a large-scale interorganizational joint emergency exercise in Sweden. A total of 20 participants from the rescue services, 12 from the emergency medical services, and 12 from the police participated in the study (N=44). Ten participants had a command-level role during the exercise and the remaining 34 were subordinates. The main outcome measures were the workload subscales of the NASA TLX: mental demands, physical demands, temporal demands, performance, effort, and frustration. The results showed that the organizations experienced different levels of workload, that the commanders experienced a higher workload than the subordinates, and that two out of three organizations fell below the twenty-fifth percentile of average workload scores compiled from 237 prior studies. The results support the notion that the NASA TLX could be a useful complementary tool to evaluate exercise designs and outcomes. This should be further explored and verified in additional studies.

An empirical comparison of relocation strategies in real-time ambulance fleet management

In order to ensure an adequate service to the population, Emergency Medical Services (EMS) rely on a given number of ambulances strategically located over the territory they serve. The arrival of calls to EMS being highly uncertain and dynamic, it may happen that at some point, the vehicles available to respond to these calls no longer cover properly all regions, even if the coverage was carefully planned initially. Relocation of ambulances may therefore be required during the day in order to achieve better performances. Some models tackling relocation have been proposed in the literature and it has been shown that using such strategies can help to improve overall performances. However, relocation generates movements that produce undesirable consequences from both economical and human resources management standpoints. Questions therefore arise: Is the relocation worth the effort? And if so, what form should it take? Unfortunately, this issue has not been investigated much up to now. This study thus focuses on evaluating and analyzing relocation strategies, and reports extensive simulation experiments allowing to analyze the performance of these strategies when the system faces different levels of workload. Our empirical study confirms that dynamic strategies dominate static ones and quantifies the improvements achieved with respect to service level, but also shows that such improvements are obtained at the expense of significant relocation costs.

Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload

Cognitive workload is an important indicator of mental activity that has implications for human-computer interaction, biomedical and task analysis applications. Previously, subjective rating (self-assessment) has often been a preferred measure, due to its ease of use and relative sensitivity to the cognitive load variations. However, it can only be feasibly measured in a post-hoc manner with the user's cooperation, and is not available as an online, continuous measurement during the progress of the cognitive task. In this paper, we used a cognitive task inducing seven different levels of workload to investigate workload discrimination using electroencephalography (EEG) signals. The entropy, energy, and standard deviation of the wavelet coefficients extracted from the segmented EEGs were found to change very consistently in accordance with the induced load, yielding strong significance in statistical tests of ranking accuracy. High accuracy for subject-independent multichannel classification among seven load levels was achieved, across the twelve subjects studied. We compare these results with alternative measures such as performance, subjective ratings, and reaction time (response time) of the subjects and compare their reliability with the EEG-based method introduced. We also investigate test/re-test reliability of the recorded EEG signals to evaluate their stability over time. These findings bring the use of passive brain-computer interfaces (BCI) for continuous memory load measurement closer to reality, and suggest EEG as the preferred measure of working memory load.

An empirical investigation of the relationship between workload and performance

Purpose – The purpose of this paper is to examine the effect of workload on quantitative and qualitative job performance. Different levels of workload can affect performance of employees, and it is important for firms to assess the effect of this in order to improve capacity decisions. The literature is not entirely clear on the relationship and calls for further empirical evidence on that matter. Design/methodology/approach – The study uses field data from a mid-sized grocery supplier. In total, 9,210 observations of 27 employees over three years and eight months are analyzed with different statistical models. Employees all work in the same department so that it is a very homogenous data set. Findings – Results show that there is an inverted U-shape relationship between workload and performance. Output of employees increases up to a certain point after which it decreases. Similarly, the quality of performance is highest under moderate levels of workload, which provides evidence against a tradeoff between quantity and quality. Research limitations/implications – The study uses a unique set of data from one firm, which limits generalizability, but adds to an important stream of literature. Practical implications – Results show how workload has a direct effect on performance. Consequently, firms need to balance the workload in order to be able to maximize the performance of their employees. Originality/value – Despite the relevance of the topic, there is hardly any empirical evidence on the relationship between workload and performance. This study thus contributes to the management literature and provides significant evidence on an inverted U-shape between workload and quantitative performance.

Combining feature extraction and classification for fNIRS BCIs by regularized least squares optimization.

In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO(2) and HbR signals. We show that this transformation can achieve competitive results in an fNIRS BCI classification task, as it significantly improves recognition of different levels of workload over previously published results on a publicly available n-back data set. Furthermore, we visualize the learned models and analyze their spatio-temporal characteristics.

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.

Modelling alarm management workflow in healthcare according to IHE framework by coloured Petri Nets

Ensuring patient safety in medical device networks by managing alarms and related clinical data is a life-critical issue. The Integrating the Healthcare Enterprise (IHE) initiative emerged to discuss and solve the interoperability and integration problems among medical information systems, vendors and users in order to improve patient care and healthcare system dependability. This paper models and analyzes the IHE Alarm Communication Management by using the Unified Modelling Language and Coloured Timed Petri nets. Aiming at generality, the model does not refer to a specific healthcare context but it is based on a general scheme where the message transactions are integrated with the nurse responses. In order to show the potentialities of the model, a real case study is simulated and different scenarios with different levels of workload are analyzed. The results illustrate that the model is able to provide support for structured and comprehensive analysis of the healthcare system management.

Modelling and Validation of Alarm Management Workflow in Healthcare Integrating IHE-PCD Profile and Coloured Petri Nets

The application of discrete event simulation modeling techniques for studying healthcare systems is a relatively new but rapidly growing field of research and service improvement. This paper discusses the application of discrete event simulation in modeling the complex relationship between Patient Care Device (PCD) in healthcare enterprises in an Alarm Communication Management (ACM) system for more efficient, integrated and validated implementation. In particular, we analyze and test healthcare processes in which a large number of different medical devices is used, at hospital departmental level with an alarm management system. Aiming at generality, the model does not refer to a specific healthcare context but is based on the general modeling schemes proposed in this field within the Integrating the Healthcare Enterprise (IHE) initiative. To this aim, we model and simulate an alarm management system implemented according to the IHE Technical Framework PCD ACM profile in a Colored Timed Petri Net framework. The model is used to test scenarios of alarm management of healthcare devices with different levels of workload and resources to demonstrate the potential of using Colored Timed Petri Net and simulation as cost effective methods for understanding the issues of healthcare operations management.

Crossmodal Links in Attention in the Driving Environment: The Roles of Cueing Modality, Signal Timing, and Workload

Multimodal information presentation has been proposed as a means to support timesharing in complex data-rich environments. To ensure the effectiveness of this approach, it is necessary to consider performance effects of recently discovered crossmodal spatial and temporal links in attention, as well as their interaction with other performance-shaping factors. The main goals of this research were to confirm that performance effects of crossmodal links in spatial attention scale to complex environments and to examine how these effects vary as a function of cue modality, signal timing, and workload. In the present study, set in a driving simulation, spatially valid and invalid auditory and tactile cues preceded the presentation of visual targets at various stimulus-onset asynchronies and under different levels of workload induced by simulated wind gusts of varied intensity. The findings from this experiment confirm that visual target identification accuracies and response times are, overall, more accurate and faster when validly-cued. Significant interactions were found between cue validity, stimulus onset asynchrony (SOA), and cue modality, such that valid tactile cueing is most beneficial at shorter (100–200 ms) SOAs, while valid auditory cueing resulted in faster responses than invalid cueing at 500 ms SOAs, but slower responses at 1000 ms SOAs. Tactile error rates were significantly higher than auditory error rates at various interactions of modality and SOA. These findings were robust across all workload conditions. They highlight the need for context-sensitive information presentation and can inform the design of multimodal interfaces for a wide range of application domains.