Workload Assessment Research Articles

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

Delivering Quality WIL Without Compromising Wellbeing: Exploring Staff and Student Wellbeing in a WIL Context Through the Lens of Organisational Health

Recent scholarship has highlighted the need to be attentive to the student experience of placement-based work-integrated learning and its possible impacts on the wellbeing of student participants. The experiences of staff involved in planning, delivering and supporting work-integrated learning programs and the impact on their wellbeing have received less attention. Using data from a survey conducted at an elite Australian university, this article explores staff perspectives on, and experiences of, work-integrated learning. Through the theoretical lens of organisational health, this article proposes key contributors to ensuring quality learning outcomes for students without comprising the wellbeing of staff. These include conducting realistic workload assessments and providing staffing and allocating workload in line with these; providing appropriate training, staff recognition and reward, and employment which recognises work-integrated learning as a specialist skillset; and resourcing skilled administrative support and technological systems.

Pengaruh Motivasi, Beban Kerja Dan Penilaian Kerja Yang Memiliki Dampak Pada Kepuasan Kerja Guru SMP Negeri 3 Lasem

The purpose of this research activity is to prove and explain the impact of motivation variables,workload and work assessment on teacher job satisfaction at SMP Negeri 3 Lasem. The researchpopulation consisted of 34 teachers from the school, and saturation techniques were used to takesamples from the entire population. Data was collected through a questionnaire survey, and data wasanalyzed through multiple regression. This research obtained results where individual workloadvariables had no significant effect on job satisfaction, while individual motivation and workassessment variables had a significant effect on teacher satisfaction. The dependent variable of jobsatisfaction of 93.6% can be explained by three independent variables: motivation, workload, andwork assessment. Other variables outside the research accounted for 6.4% of the dependent variablejob satisfaction.

Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring.

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.

Assessment of Mental Workload of Nurses Using Optimized NASA-TLX Index Based on Fuzzy Approach

Assessment of Mental Workload of Nurses Using Optimized NASA-TLX Index Based on Fuzzy Approach

A Study on Workload of a Medical Linear Accelerator at a High Throughput Cancer Treatment Centre in Sri Lanka

Radiotherapy bunker design depends on several factors, and among them, workload is one of the important factors. Workload is calculated from the radiation dose delivered at the isocenter. Due to the introduction of new radiotherapy treatment modalities and an increasing number of new cancer patients, there is a higher possibility to increase the workload. Therefore, this study aims to assess the workload of Varian linear accelerator vault at Apeksha Hospital, Maharagama. Data were collected from 1st of August 2020 to 30th November 2020. Dose delivered at isocenter from the 3D-CRT procedures with 6 MV and 15 MV photon energy, the IMRT procedures with 6 MV photon energy and the quality assurance (QA) procedures were collected from ARIA patient management system and transferred into Excel spread sheet for data analysis. The calculated weekly workload was 2326 Gy/week with a 43% contribution from the IMRT procedures with 6 MV, 39% contribution from the 3D-CDT procedures with 6 MV, 16% contribution from the 3D-CDT procedures with 15 MV and 2% contribution from physics workload (QA procedures). The evaluated workload is higher than the NCRP recommended workload of 1000 Gy/week. This study recommends for at least a one-year survey for more accuracy on workload assessment, and also to evaluate the use factor since most of the advanced radiotherapy treatment techniques use high number of monitor units which will increase the leakage radiation.

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes.

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.

Integrated Spatio-Temporal Deep Clustering (ISTDC) for cognitive workload assessment

Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of n-back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment.

NEP+: A Human-Centered Framework for Inclusive Human-Machine Interaction Development.

This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform's dual mission: to empower the network-related capabilities of ZeroMQ and to provide software tools and interfaces for prototyping and integration. NEP+ accomplishes this through a comprehensive quality model and an integrated software ecosystem encompassing middleware, user-friendly graphical interfaces, a command-line tool, and an accessible end-user programming interface. This article primarily focuses on presenting the proposed quality model and software architecture, illustrating how they can empower developers to craft cross-platform, accessible, and user-friendly interfaces for various applications, with a particular emphasis on robotics and the Internet of Things (IoT). Additionally, we provide practical insights into the applicability of NEP+ by briefly presenting real-world user cases where human-centered projects have successfully utilized NEP+ to develop robotics systems. To further emphasize the suitability of NEP+ tools and interfaces for developer use, we conduct a pilot study that delves into usability and workload assessment. The outcomes of this study highlight the user-friendly features of NEP+ tools, along with their ease of adoption and cross-platform capabilities. The novelty of NEP+ fundamentally lies in its holistic approach, acting as a bridge across diverse user groups, fostering inclusivity, and promoting collaboration.

The effects of increased mental workload of air traffic controllers on time perception: Behavioral and physiological evidence

Research has shown that timing is modulated by mental workload, making duration judgments a measure of cognitive demand, alongside subjective assessments, and physiological measurements. Yet, it is unclear whether such findings can be extended in less controlled setups. By employing air traffic controllers in a real aviation environment, we tested whether tasks with different levels of cognitive load can affect their timing behavior. Participants completed temporal production, verbal estimation, and passage of time judgments, while actively engaging in real flight control sessions. Subjective assessments of task demands, as well as physiological responses (cardiac and electrodermal activity) were also measured. Accuracy of the produced intervals was measured at two distinct phases of the flight (during low-load cruising vs. high-load landing) and under two different task load manipulations (controlling one vs. two helicopters and speaking in native vs. non-native language). Analysis of interval production accuracy showed that during the high-load landing phase significant overproductions were made, compared to the low-load cruising phase, and landing two helicopters led to greater overproductions compared to landing only one. The duration of the two-helicopter sessions was significantly overestimated compared to the single-helicopter ones, and the passage of time was felt significantly faster. Subjective assessments of workload were positively correlated with the temporal estimations and passage of time judgments, and skin responses were positively correlated with the produced intervals. Overall, our results are consistent with past research, suggesting that mental workload modulates time perception in complex, real-world environments, thus making timing behavior a reliable index of the workload changes.

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Does augmented reality help in industrial training? A comprehensive evaluation based on natural human behavior and knowledge retention

The complexity and diversity of products significantly increases the difficulty of training workers in modern industries. AR training, as a combination of industrial digital development and new interactive means, has attracted increasingly more attention. However, in human factor and education, research regarding AR based training is still in its infancy. To understand how AR could help industrial training from a human perspective, we evaluate a HoloLens-based AR training method for an assembly task on natural human behavior and the training effectiveness. Forty-four participants were equally divided into AR and ordinary training groups with the equivalent instructional materials, which included text information and 3D models. Results show that although AR did not significantly affect the overall workload and system usability assessment (SUS) recorded, several experimental values increased significantly compared with the ordinary training method, including task completion time, time required to interpret text-based information, amount of attention switching, and training effectiveness. Consequently, the use of AR did not result in a completion time and workload reduction; however, we found that training with the AR system was more effective than ordinary training in increasing the knowledge retention of participants without increasing their overall workload, especially for the month-term memory. Nonetheless, a longer time was required by the users to familiarize themselves with the AR environment and read the text information. The findings of this work could contribute to the design of future AR training systems in industry considering natural human behavior and enhance training effectiveness.

An Analysis of Physiological Workload on Pure Coconut Oil Production Process

This study aims to analyze the physiological workload in the virgin coconut oil production process based on energy expenditure evaluations and determine rest periods to ensure that work does not require excessive energy. Assessment of workload objectively using pulse indicators and energy consumption (energy cost) observations at the four stages of the virgin coconut oil production process, namely the process of stripping the coconut husk, the process of cutting the coconut shell, the process of grating the coconut and the process of cooking the coconut milk. The energy expenditure of workers in the coconut coir stripping section is 5,138 kcal/min, the coconut shell cutting section is 2,552 kcal/min, the coconut meat grating section is 3,575 kcal/min, and the coconut milk cooking section is 2,885 kcal/min. Hence the coco coir stripping workstation is included in the heavy workload category with an average cardiovascular load value of %CVL of 56%, which means that the work needs to be improved; the recommended work-rest cycle for workers at the coco peeling workstation is 47 minutes. The workstation for cutting coconut shells to cooking coconut milk is included in the category of moderate workload with a value of %CVL ≤33%, still within the recommended workload limit.

An Analysis of Physiological Workload on Pure Coconut Oil Production Process

This study aims to analyze the physiological workload in the virgin coconut oil production process based on energy expenditure evaluations and determine rest periods to ensure that work does not require excessive energy. Assessment of workload objectively using pulse indicators and energy consumption (energy cost) observations at the four stages of the virgin coconut oil production process, namely the process of stripping the coconut husk, the process of cutting the coconut shell, the process of grating the coconut and the process of cooking the coconut milk. The energy expenditure of workers in the coconut coir stripping section is 5,138 kcal/min, the coconut shell cutting section is 2,552 kcal/min, the coconut meat grating section is 3,575 kcal/min, and the coconut milk cooking section is 2,885 kcal/min. Hence the coco coir stripping workstation is included in the heavy workload category with an average cardiovascular load value of %CVL of 56%, which means that the work needs to be improved; the recommended work-rest cycle for workers at the coco peeling workstation is 47 minutes. The workstation for cutting coconut shells to cooking coconut milk is included in the category of moderate workload with a value of %CVL ≤33%, still within the recommended workload limit.

A review of physiological measures for mental workload assessment in aviation

AbstractThe relevant growth of human-machine interaction (HMI) systems in recent years is leading to the necessity of being constantly aware of the cognitive workload level of an operator, especially in a safety-critical context such as aviation. Since the confusion in the definition of this concept, this paper clarifies this terminology and also highlights its relationship with stress. Thus, we analysed the state-of-the-art of cognitive workload evaluations, showing three up-to-date methodologies: subjective, behavioural and physiological. In particular, the physiological approach is increasingly gaining attention in the literature due to today’s exponential growth of biomedical sensors. Therefore, a review of the most adopted physiological signals in the workload evaluation is provided, focusing on the aeronautical field. We conclude by highlighting the necessity of a multimodal approach for mental workload assessment as a result of this analysis.

Building motivation above trust: A blockchain-based NFT GameFi model for construction workload assessment

In the VUCA (Volatile, Uncertain, Complex, Ambiguous) era, construction management faces unique challenges, particularly in the realm of workload assessment. This paper proposes an innovative solution to address these challenges: a blockchain-based GameFi model for construction workload assessment. This model leverages blockchain Non-Fungible Token (NFT) technology to issue various badges, reliably representing employees' workload. The technology ensures the authenticity and tamper-proof nature of the badges, thereby fostering employee motivation through a distributed trust system. This study provides fresh insights into the construction industry's digital transformation and is anticipated to lay the groundwork for similar applications in other sectors.

Using a Quality Improvement Approach to Implement Measurement-Based Care (MBC) in Outpatient General Psychiatry.

Measurement-based care (MBC) is the clinical practice of using patient-reported symptom measurement to inform treatment decisions. MBC has been shown to improve patient outcomes and quality of care in outpatient psychiatry. Despite these benefits, MBC is not routinely used in most psychiatric outpatient settings. This quality improvement (QI) project aimed to achieve 75% completion of symptom scales using an online MBC platform in a general psychiatry clinic in Toronto, Canada, by June 2022. The QI team used the Model for Improvement methodology. The main outcome measure was completion of symptom scales using an online MBC platform. Process measures included counts of invitations to join the MBC platform, counts of online MBC account creation, and counts of symptom scale assignment by clinicians. Balancing measures included administrative task completion and physician workload assessment. Stakeholder interviews explored barriers and facilitators to MBC utilization. Completion of symptom scales increased from 7/65 (10.8%) preintervention to 40/70 (57.1%) during the 26-week intervention. Clinician reminders and coaching clinicians about how to incorporate MBC into the care process facilitated uptake of MBC. Stakeholders identified several barriers to implementation, particularly physician attitudes toward MBC and perceived administrative burden. Completion of symptom scales increased over the course of this QI initiative. Successful implementation of MBC in general psychiatry outpatient settings requires the availability of a broad range of measurement scales given the diversity of mental health diagnoses. Implementation must also consider health equity, including access to technology and availability of symptom scales in multiple languages.

60 NICU Manager’s Perception of High Unit Activity and Its Association with Patient Care in the Neonatal Intensive Care Unit

Abstract Background Each year, 15,000 critically ill neonates require specialized care that is offered in only 32 tertiary neonatal intensive care units (NICUs) in Canada. Previous studies have shown associations between nurse-to-patient ratios and higher bed occupancy with patient outcomes. Objective To explore neonatal managers’ perceptions of organizational factors in Canadian NICUs. Methods This was a web-based cross-sectional survey, consisting of 20 questions pertaining to the determinants of resource allocation, the ascertainment of high occupancy state, as well as the different challenges and mitigation strategies implemented during periods of high unit strain. The survey was designed following the CHERRIES guidelines and was critically reviewed through a two-round validation process prior to deployment. It was sent by e-mail to the unit managers of all Canadian Level-3 NICUs between August and November 2022. They were invited to respond based on their unit’s practices in 2021. The analysis was conducted using descriptive statistics, where units were dichotomized by size (small unit ˂36 beds, large unit ≥36 beds). Results A total of 24 unit managers (75%) completed the survey. Most respondents relied exclusively on clinical judgment to estimate the total number of nurses required per shift (33%) and the individual nurse-to-patient ratios (67%) as opposed to nursing workload assessment tools (8% and 21%, respectively). The response for the proportion of nursing shifts perceived as adequately staffed was: 0% for 100% adequately staffed, 37% for ˃80% adequately staffed, and 27% for 60%-80% adequately staffed. The response for the proportion of nursing shifts perceived as understaffed to the point of being unsafe for patient care was: 17% for no understaffed, 52% for ˂20% understaffed, and 9% for 20%-40% understaffed. The most common organizational challenges were personnel recruitment and unit occupancy (each reported as being a major issue by 38% of respondents). Although most (75%) respondents believed that high occupancy, especially when exceeding 90%, likely increases the risk of adverse patient outcomes, they revealed implementing mitigation strategies to reduce occupancy only when it exceeded 95%. At that point, the most common strategies implemented were to call for additional voluntary nurses, implement voluntary nursing overtime, and accelerate patient transfer. Conclusion Although Canadian NICUs form a very heterogeneous group, managers shared similar perspectives in terms of staffing and occupancy challenges. This emphasizes the need for collaborative NICU person-power resource management practices to improve neonatal intensive care service organization.

Analysis of Physical and Mental Workload on Emergency Department (ED) Nurses with Subjective Workload Assessment Technique (SWAT) and Cardiovascular Load (CVL) Methods (Case Study of Sidoarjo City Hospital)

Analysis of Physical and Mental Workload on Emergency Department (ED) Nurses with Subjective Workload Assessment Technique (SWAT) and Cardiovascular Load (CVL) Methods (Case Study of Sidoarjo City Hospital)

Understanding Systemic, Organizational and Individual-Specific Factors to Minimize Burnout

Burnout has a significant impact on the individual in terms of their physical and psychological well-being. The negative flow-on effects on the organization’s productivity and workforce, in general, are also important to recognize. In the healthcare sector, burnout of clinicians can impact the quality of care and patient safety outcomes. It is important and necessary to understand the contributing systemic, organizational, and individual-specific factors to develop a holistic response to prevent burnout. An integrated approach should be aimed at the systems level, which requires continual monitoring and assessment of resilience and workloads; at the organizational level, identifying factors unique to the organization type and setting; and at the individual level, focused on their well-being.