Workload Assessment Research Articles

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller’s cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.

User technology for AI-supported objective measurement of Work-related Quality of Life (MI-LQ)

Abstract Background Understanding and enhancing job satisfaction is crucial for improving Work-Related Quality of Life (WrQoL) in today’s office work environments. Our work uses multimodal data collection and analysis techniques to objectively assess WrQoL, thus mitigating respondent bias and survey fatigue. Leveraging machine learning (ML) and automated methods measuring WrQoL presents a promising approach in public health research, particularly concerning stress-related health outcomes. Methods Through a systematic literature search of 9,242 publications to identify WrQoL indicators, we selected over 100 publications for data extraction. A base model, including workload, commute, overtime, valence, and arousal was formulated. To amass a wide data set, Objective measurement tools (computers, smartphones, and wearables) complemented by questionnaire data. This data will inform ML algorithms to predict individual WrQoL indicators. Developed as an input/output data platform, an app-prototype will objectively assess WrQoL, facilitating a field study with office workers to refine the model. A mobile app prototype serves as a user interface and data relay for WrQoL metric analysis. Results Our research develops instruments for measuring WrQoL using smartphones, wearables, and specialized trackers for heart rate variability (HRV) analysis, alongside a software prototype for keyboard and mouse tracking for workload assessment of valence and arousal. Fitness trackers with photoplethysmography functionality are favored for HRV assessment. Conclusions The systematic search identified influences on WrQoL, translated into measurable indicators in a base model. Leveraging user tech for assessment and a ML and mobile app framework, this approach facilitates objective WrQoL determination. By presenting the relationship between digital service use and WrQoL indicators, this methodology promises new insights, informing stress and burnout prevention strategies within public health initiatives. Key messages • Progressive approach: ML-driven WrQoL assessment offers promising avenues for stress prevention in workplace health research. • Methodological Advancement: The novel framework for WrQoL assessment using digital tools contributes to effective workplace health promotion strategies, with potential for tailored interventions.

Designing Ergonomic Standing Chair for Computer Numerical Control Machine Operators using TRIZ

In this research, the working hours and posture of CNC (Computer Numerical Control) machineoperators were investigated to determine the physical workload that they experienced during theirdaily work. Based on our observations, the operator works 8-hour regular shifts or 12-hour longshifts for 5 – 6 days a week. During these shifts, the operators work in front of the CNC machinewhile standing upright without any tools to rest. Preliminary research was conducted to evaluatethese conditions the results show 13 out of 20 CNC machine operators complained about physicalfatigue while working the CNC machines. It can also be concluded that the operators areexperiencing excessive workload based on a workload assessment using the Quick Exposure Check(QEC) method. To solve this problem, a standing chair is designed with ergonomics in mind. Thedimensions of the standing chair are based on the anthropometry of the CNC machine operators,which was never used before. The design process is conducted using the Theory of InventiveProblem Solving (TRIZ) methodology. Need statements from the CNC machine operators anddesign requirements from the stakeholders are converted into design specifications using TRIZmethodology. Parameters are determined to increase the capability of the existing design based onthe design specifications. The result of this research is a standing chair that is suitable for theoperator’s needs. Based on the analysis of the design result and the stress analysis simulation, it canbe concluded a functional standing chair can be ergonomically designed with the TRIZmethodology.

Investigating mental workload across task modalities: a multimodal analysis using pupillometry

Understanding mental workload is challenging due to its multidimensional nature and varying sensitivities of its primary measures: task performance, perceived workload, and physiological responses. This study investigates the effects of task load on performance, perceived workload, and pupil measures across different information modalities. A within-subjects experiment involving three tasks (digit span, matrix span, and dual n-back) was conducted with three task load levels. Workload measures included accuracy/sensitivity, reaction time, NASA-TLX, peak pupil diameter, and peak pupil latency. Consistent patterns of associations between task load and these workload measures were found across the three modalities. Workload measures revealed a nuanced interplay between task modality and task load. Robustness of peak pupil latency, accuracy, and NASA-TLX was highlighted across verbal and visual modalities, while peak pupil diameter showed a weaker impact with differences between modalities. The findings encourage multivariate assessment of mental workload to account for different task modalities.

Pathologist Workload Assessment in Gastrointestinal Subspecialty: A Pilot comparative study in an academic setting

Abstract Introduction/Objective There are no official guidelines regarding the workload to be received by pathologists, and it is challenging to distribute equivalent workloads across subspecialties. This study aims to determine whether slide count is a reliable workload metric in gastrointestinal (GI) pathology. Additionally, we seek to compare the time allocation differences between GI pathologists involved in medical resident teaching and those dedicated only to anatomical pathology duties. Methods/Case Report This is an observational retrospective cohort study. We recorded the first two hours of a workday of three experienced GI pathologists at Stanford Healthcare in 2022, two of whom were engaged in teaching. The authors carefully retrospectively reviewed the recorded videos to extract relevant information: teaching activities, number of histological slides, specimens, types of tissues analyzed, and resultant diagnosis. Results (if a Case Study enter NA) Two attendings (2 and 3) signed out with residents, while only one worked alone (1). The mean time spent on each case was 199 and 486 seconds different between attendings 1 and 2 and 1 and 3, respectively (p<.01). There was a strong positive correlation between the number of slides and time spent analyzing each case, r =.607. The number of slides per case statistically explained 36.8% of the time spent per case, p <.001. Conclusion In conclusion, the slide count metric only explains 36.8% of the time expenditure, suggesting that more sophisticated workload assessment methods must be developed, particularly if used for workload distribution purposes. Also, GI pathologists involved in medical training take longer to sign out cases than GI pathologists who are not, suggesting that this is an essential factor that should be considered when distributing workloads.

Mental load in University Teachers: Case study

Mental load among university teachers has gained importance in recent decades due to its impact on workers' health and well-being. This study focuses on the mental load of teachers from the Faculty of Engineering at the National University of Chimborazo, analyzing factors like the cognitive and emotional demands they face in their daily work. The aim is to determine mental load levels and their relationship with sociodemographic variables, identifying the need for preventive interventions. The Subjective Workload Assessment Technique (SWAT) was applied to a sample of 97 teachers who voluntarily completed the survey. Results showed that 80.4% of teachers exhibit medium mental load levels, while 18.6% report high levels. Significant correlations were identified between mental load and variables like age and gender. Stress and mental effort dimensions were particularly high, indicating a potential risk to teachers' health. It is concluded that implementing preventive measures and intervention programs is necessary to improve working conditions and teacher self-efficacy.

Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

AbstractThe workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real‐time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA‐TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA‐TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k‐nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

Adaptation of the Nursing Activities Score in Latvia.

This study focuses on the adaptation and validation of the Nursing Activities Score (NAS) for use in Latvian intensive care units (ICUs) to measure nursing workload. The NAS, widely used internationally, was selected for its comprehensive ability to reflect 81% of ICU nursing activities, making it a suitable tool for assessing nursing workload in the Latvian healthcare context. The study followed a two-phase methodology: (1) expert validation using the Content Validity Index (CVI) and (2) a pilot study to assess the psychometric properties of the adapted tool. In the first phase, 10 ICU nursing experts assessed the translated NAS items, resulting in revisions to three specific paragraphs (4a, 14, and 20) based on low CVI scores. After refinement, CVIs improved from 0.6 to 0.8 for paragraphs 4a and 14, and from 0.5 to 0.9 for paragraph 20. The final CVI for all items reached 0.909. In the second phase, a pilot study was conducted in a Latvian ICU with 42 patients and 226 NAS assessments. The psychometric evaluation showed strong reliability and validity, confirming the NAS's suitability for measuring nursing workload in this context. Cronbach's alpha for the scale was 0.973. The adapted NAS provides a standardised method for workload assessment in Latvian ICUs, offering potential improvements in nurse resource management and patient care.

Study on the Driver Visual Workload in High-Density Interchange-Merging Areas Based on a Field Driving Test.

A visual workload model was constructed to determine and evaluate drivers' visual workload characteristics in high-density interchange-merging areas. Five interchanges were selected, and a real-vehicle driving test was conducted with 47 participants. To address the differences in drivers' visual characteristics in the interchange cluster merging areas, the Criteria Importance Through Intercriteria Correlation (CRITIC) objective weighting method was employed. Six visual parameters were selected to establish a comprehensive evaluation model for the visual workload in high-density interchange-merging areas. The results show that the average scanning frequency and average pupil area change rate are most strongly correlated with the visual workload, whereas the average duration of a single gaze has the lowest weight in the visual workload assessment system. Different driver visual workloads were observed depending on the environment of the interchange-merging areas, and based on these, recommendations are proposed to decrease drivers' workload, thereby increasing road safety.

Commentator Discussion: A novel, multimodal, intraoperative cognitive workload assessment of cardiac surgery team members

Commentator Discussion: A novel, multimodal, intraoperative cognitive workload assessment of cardiac surgery team members

Assessment of cognitive workload using simultaneous EEG and fNIRS: A comparison of feature combinations

The assessment of cognitive workload is a critical component in evaluating mental activity. It is essential in psychology, especially in professions with heightened cognitive demands, such as aviation and surgery. Even marginal improvements in workload assessment can substantially enhance safety and overall performance. This study aimed to improve the accuracy of cognitive workload assessment by employing a combined EEG+fNIRS approach with a minimal number of features. The Tunable Q-factor Wavelet Transform (TQWT) was applied to signals from EEG and fNIRS to extract relevant features. TQWT was chosen for its ability to adapt to the oscillatory behavior of the signal, and the features extracted from the transform facilitated the differentiation between different signal classes. This study introduced six proposed approaches, with the first focusing on EEG signals, the second on fNIRS signals, and the subsequent three to six approaches involving combinations of these two signals. Features evaluation highlighted the pivotal role of channels related to the frontal region and sub-bands associated with alpha and theta rhythms in EEG signals. Additionally, the very low-frequency range and the primary undecomposed signal in fNIRS signals contributed significantly to cognitive workload classification. The classification results demonstrated the efficacy of combining EEG and fNIRS signals in cognitive workload detection, providing higher accuracy than single-signal approaches. Compared to other feature combination approaches, the proposed approach based on Canonical Correlation Analysis (CCA) with a linear SVM classifier achieved superior accuracy across all investigated cognitive workload levels, with accuracies of 99.6 %, 99.4 %, 98.9 %, and 97.5 % for the 0-back vs. 3-back, 0-back vs. 2-back, 2-back vs. 3-back, and 0-back vs. 2-back vs. 3-back comparisons, respectively.

Training the Next Generation of Transplant Surgeons With a 3-Dimensional Trainer: A Pilot Study.

In the United States, no published guidelines promote exposure to technical variants (ie, living donor or split liver) during transplant fellowship. Simulation with hands-on liver models may improve training in transplantation. This pilot study addressed 3 overall goals (material and model creation tools, recruitment rates and assessment of workload, and protocol adherence). A patient-specific hands-on liver model was constructed from clinical imaging, and it needed to be resilient and realistic. Multiple types of materials were tested between January 2020 and August 2022. Participants were recruited stepwise. A left lateral segmentectomy simulation was conducted between August 2022 and December 2022 to assess protocol adherence. Digital anatomy 3-dimensional printing was considered the best option for the hands-on liver model. The recruitment rate was 100% and 47% for junior attendings and surgical residents, respectively. Ten participants were included and completed all the required surveys. Seven (70%) and 6 (60%) participants "agreed" that the overall quality of the model and the material were acceptable for surgical simulation. Five participants (50%) "agreed" that the training improved their surgical skills. Nine participants (90%) "strongly agreed" that similar sessions should be included in surgical training programs. Three-dimensional hands-on liver models have the advantage of tactile feedback and were rated favorably as a potential training tool. Study enrollment for further studies is possible with the support of leadership. Rigorous multicenter designs should be developed to measure the actual impact of 3-dimensional hands-on liver models on surgical training.

Real-Time Workload Assessment to Enhance Performance.

Workforce shortages, increasing costs, decreased reimbursement, and focus on quality outcomes are crucial issues for health care leaders. To remain competitive, profitable, and productive, health care organizations need to provide structure, a safe working environment, and an acceptable leader workload to guarantee effective leader performance. Poorly designed work environments and interfaces can increase workload resulting in decreased performance and satisfaction. Excessive workload has led to reduced job satisfaction, productivity, and resilience. Due to leadership turnover and vacancy rates, leader workload was perceived to be unreasonable in the respiratory therapy (RT) department of an academic medical institution in central North Carolina. The aim of this quality initiative was to explore the workload of health care leaders in the RT department to identify the factors that influenced workload as well as implement strategies to decrease perceived workload. A workload assessment was performed, which identified inefficiencies and opportunities to partner with ancillary departments to align the workload with appropriate clinical teams. The redistribution of workload provided alignment, top of scope practice, and improved satisfaction among the RT department leaders. This article identifies the strategies utilized which can be translated to other institutions.

Using Semi-supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance.

Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.

Air Traffic Controller Workload Detection Based on EEG Signals.

The assessment of the cognitive workload experienced by air traffic controllers is a complex and prominent issue in the research community. This study introduces new indicators related to gamma waves to detect controllers' workload and develops experimental protocols to capture their EEG data and NASA-TXL data. Then, statistical tests, including the Shapiro-Wilk test and ANOVA, were used to verify whether there was a significant difference between the workload data of the controllers in different scenarios. Furthermore, the Support Vector Machine (SVM) classifier was employed to assess the detection accuracy of these indicators across four categorizations. According to the outcomes, hypotheses suggesting a strong correlation between gamma waves and an air traffic controller's workload were put forward and subsequently verified; meanwhile, compared with traditional indicators, the indicators associated with gamma waves proposed in this paper have higher accuracy. In addition, to explore the applicability of the indicator, sensitive channels were selected based on the mRMR algorithm for the indicator with the highest accuracy, β + θ + α + γ, showcasing a recognition rate of a single channel exceeding 95% of the full channel, which meets the requirements of convenience and accuracy in practical applications. In conclusion, this study demonstrates that utilizing EEG gamma wave-associated indicators can offer valuable insights into analyzing workload levels among air traffic controllers.

Exploring Cognitive Workload Changes in Teleoperators: Collaboration with On-Site Workers and Robots in Assembly Tasks

Teleoperation is commonly employed to perform industrial tasks in remote or inaccessible areas. However, there is a noticeable gap in evaluating cognitive workload of teleoperators in collaboration environments. This study compared the variation of cognitive workload for teleoperators guiding an on-site participant to complete a wire assembly task in two scenarios: one aided by a robot arm (tRH) and another without any robot assistance (HH). Additionally, the task demands for on-site participants were manipulated to measure its impact on teleoperator’s workload. NASA-TLX and EEG activity were utilized to assess workload. The results indicated that EEG theta activity was significantly higher for the HH group than tRH group, potentially showing lower workload for teleoperators in the scenario with robot assistance. Task difficulty did not affect any of the workload measures. The study highlights the importance of cognitive workload assessment in human-robot collaborations to optimize human cognitive demands in complex settings.

Study on comprehensive evaluation method of mental workload level

Abstract Mental workload has an important impact on the human performance of ship operators. The purpose of this study is to develop an evaluation method to evaluate the ship operator’s mental workload. First, an evaluation index system was constructed, and the DEMATEL (decision-making trial and evaluation laboratory) method was used to determine the relative weight of each index. Then, the fuzzy evaluation method was used to calculate the mental workload of ship operator based on the evaluation value and relative weight of indexes. The evaluation result of the developed method showed that the ship operators’ mental workload were 3.78 and 2.99 under tasks 1 and 2, and the ship operators’ mental workloads were at the ‘very high’ and ‘high’ levels based on the maximum membership principle. Also, the evaluation result of the developed method was consistent with the evaluation results of subjective mental workload assessment technique. It proves that the developed method can effectively evaluate the ship operator’s mental workload level.

What not to do in facial infrared thermographic measurements: A post data enhancement

The accuracy of infrared thermographic measurements depends on several factors, including movement of target. In this study, accuracy of nose tip temperatures obtained in a mental workload assessment using a thermal imaging camera were impacted by participants’ movement and camera zooming/panning. To correct these measurement errors, we compared manual facial landmark identification techniques using data labelling software with an automated deep learning-based approach utilised for facial landmark tracking and evaluated both against the built-in tracking features of the thermal camera, Thermal Spot Tracking. Using the Manual Thermal Landmark Annotation measurements as the ground truth, our results show that the Automated Facial Feature Tracking approach, which is the AI based approach performed better than the Thermal Spot Tracking as it matched comparatively more spatial coordinates and temperature datapoints as well as showed comparatively lower mean relative error. The study highlights the potential of AI in enhancing the accuracy of thermographic measurements, particularly in applications involving facial temperature analysis.

Utilising raw psycho-physiological data and functional data analysis for estimating mental workload in human drivers

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

Recognition of marine navigators’ workload based on eye movement features using bridge simulation

The human factor accounts for 80% of marine traffic accidents, either directly or indirectly. Workload significantly impacts the reliability of marine navigators’ behavior. Employing eye movement measurement as an objective assessment tool can effectively evaluate seafarers’ workload. This study conducts bridge simulation experiments to analyze eye movement features and investigate marine navigators’ workload levels across different tasks. By categorizing marine navigators’ workload into high and low level based on specific navigation duties. The study employs an independent sample T-test to select blink frequency, fixation time percentage, and pupil area standard deviation as key eye movement features for workload recognition. A marine navigators’ workload recognition model is developed utilizing the support vector machine (SVM) algorithm, which is subsequently trained and evaluated with experimental data. Results demonstrate a high recognition rate of 90.91% for marine navigators’ workload, indicating the SVM method’s accuracy in workload assessment based on eye movement features. These findings lay the foundation for the advancement of intelligent monitoring systems for marine navigators’ workload and the implementation of human–computer interaction active early warning technology and equipment.