Human Performance Research Articles

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, effective human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.

Performance evaluation of automated scoring for the descriptive similarity response task

We examined whether a machine-learning-based automated scoring system can mimic the human similarity task performance. We trained a bidirectional encoder representations from transformer-model based on the semantic similarity test (SST), which presented participants with a word pair and asked them to write about how the two concepts were similar. In Experiment 1, based on the fivefold cross validation, we showed the model trained on the combination of the responses (N = 1600) and classification criteria (which is the rubric of the SST; N = 616) scored the correct labels with 83% accuracy. In Experiment 2, using the test data obtained from different participants in different timing from Experiment 1, we showed the models trained on the responses alone and the combination of responses and classification criteria scored the correct labels in 80% accuracy. In addition, human–model scoring showed inter-rater reliability of 0.63, which was almost the same as that of human–human scoring (0.67 to 0.72). These results suggest that the machine learning model can reach human-level performance in scoring the Japanese version of the SST.

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD.

Around 30% of non-exudative macular neovascularizations(NE-MNVs) exudate within 2 years from diagnosis in patients with age-related macular degeneration(AMD).The aim of the study is to develop a deep learning classifier based on optical coherence tomography(OCT) and OCT angiography(OCTA) to identify NE-MNVs at risk of exudation. AMD patients showing OCTA and fluorescein angiography (FA) documented NE-MNV with a 2-years minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented MNV exudation within the first 2 years formed the EX-GROUP while the others formed QU-GROUP.ResNet-101, Inception-ResNet-v2 and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the 6 models were evaluated with major and soft voting techniques. Eighty-nine (89) eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited(35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the 3 single convolutional neural networks(CNNs).The major voting model resulting from the association of the 3 different CNNs resulted in improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders' performance). Soft voting model resulting from the combination of OCTA and OCT B-scan based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA enface imaging were more prevalent in QU GROUP. Artificial intelligence shows high performances in identifications of NE-MNVs at risk for exudation within the first 2 years of follow up, allowing better customization of follow up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.

Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images

BackgroundArtificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on...

Artificial intelligence to advance acute and intensive care medicine.

This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.

Athlete Health and Human Performance Will Not Improve Without Transdisciplinary Collaboration and Data Sharing in Elite Sport.

There are two largely competing models for an athletics organization at both the collegiate and professional levels: the High Performance Model and the Medical Model. The High Performance Model arises from international Football perspective that places a "Performance Director" at the center of teams supporting the athletes. The Medical Model, supported by both the National Athletic Trainers Association and the NCAA, separates off medical staff (athletic trainers, physical therapists, and physicians, predominantly) and emphasizes the autonomy of medical decisions. The Medical Model has left clinicians in a "medical silo", limiting our ability to care for the individual athletes as holistic people and limits our wider impact in the field of athlete health and injury mitigation. We argue that Medical. Model is consistent with the High Performance Model only if we reject the notion that the "Performance Director" is an administrative person and instead conceptualize this as a "Health and Performance Information Hub" which facilitates transdisciplinary collaboration. This Commentary details how a data broker system can be used to accelerate transdisciplinary collaboration within an athletic organization, leading to better healthcare for athletes and improved team and individual performance. Furthermore, a transdisciplinary organization with data sharing is able to turn real-world data into real-world evidence, enhancing the care and performance of athletes locally as well as facilitating the creation of generalizable knowledge in the area of sports medicine and human performance.

Human performance and automated operations: a regulatory perspective

Increased levels of digitalisation present major opportunities for efficiency in the oil and gas industry but can also contribute to new risks and vulnerabilities. Based on developments in the industry, the Norwegian Ocean Industry Authority (HAVTIL) has in recent years pursued targeted knowledge development and follow-up of company’s digitalisation initiatives. This paper explores data collected through HAVTIL’s audits of the development and use of automated systems within well operations. The analysis of the data resulted in the identification of five main topics related to the implementation of digital technologies. The five main topics were organisational complexity, follow-up and implementation of technology, analysis and documentation, user-interface and alarms and competence and training. Overall, the results support research findings within human factors and technology development, pointing out that there is a lack of focus on human factors in both development projects and in operations. In addition, this paper provides insight into how digitalisation initiatives are followed-up and explores the results from the analysis in light of the current developments in the industry.

Exploring factors in Human Cognition that affect Human Performance during Takeover in Autonomous Driving

Driver's mental load is an important influence on the design of the human-machine interface (HMI) for self-driving cars. During the autopilot-forced takeover process, aspects of the user's cognition, decision-making, attention, and emotional state affect the takeover performance. Therefore, the cognitive and psychological states of drivers must be considered in the HMI design of self-driving cars. And a good HMI design will reduce the user's mental burden and improve the takeover performance. In this study, a model affecting drivers' takeover performance was designed through human factors. And we refine the analysis from three aspects of cognitive load, emotional load, and environmental load by making subjective likert-style-questionnaires, derive the main influencing factors through the reliability analysis of the data, factor analysis, and other methods. This work finds that the clarity, comprehensiveness, and level of humanization of takeover messages, as well as the driver's arousal level when receiving the message, have the greatest impact on the driver's workload.

HRM practices, organizational learning and organizational performance: evidence from the big four financial services in France

Purpose This paper aims to examine the mediation effect of organizational learning on the link between human resource management (HRM) practices and organizational performance in some Big4 financial services companies. Design/methodology/approach The quantitative methodology was chosen for this research, using resource theory and knowledge-based approach to explain the relationship between latent variables. A sample of 403 HR employees and managers of the companies under study in France was selected in 2022. Structural equations modeling was used based on the Spss-Amos program to test the research hypotheses. Findings The results revealed that organizational learning played a mediating role between HRM practices (hiring, training, motivation and decision-making) and organizational performance and that learning enabled the performance of workers to improve and achieve competitive advantages in this field. Research limitations/implications The sample was based on four international companies working in the field of financial services and consulting and providing their services within France, which may affect the generalisability of the results and limit them to the studied sector. Practical implications The contribution of the study is to improve the awareness of administrators, decision makers and company employees of the importance of organizational learning for companies, and to stimulate motivation to learn and exchange knowledge in a constructive way that enhances organizational performance. Working on organizational culture change through HRM-practices-based learning as an effective mechanism for organizational performance improvement is one implication. These practises influence cadres' attitudes toward their work, which improves their performance. Social implications Working on organizational culture change through HRM-practices-based learning as an effective mechanism for organizational performance improvement is one implication. These practises influence cadres' attitudes toward their work, which improves their performance. Originality/value This study seeks to provide cadres and executives with an in-depth analysis of HRM and organizational learning, which, through its integration of these attributes, can contribute to the earning of knowledge-based competitive advantage and achieve superior and sustainable performance.

Accounting for spatial coupling to assess the interactions between human well-being and environmental performance

Understanding the spatio-temporal interactions between ecosystem services (ESs) and the progress of Sustainable Development Goals (SDGs) is crucial in arid regions due to the more intense conflicts between human well-being and environmental performance. However, the interactions over distance and their spatial connectivity network among regions are often neglected, influencing the advancements in policy making especially in arid basin. Here a comprehensive framework that integrated ESs and SDGs was proposed to assess the spatial coupling between human well-being and environmental performance within and across regions in the Heihe River Basin (HRB), the second largest inland river basin in China. Then the Random Forests method was employed to identify the key driving factors of the interactions. The results are as follows: (1) The coupling that accounts for both local and distant effects was higher than that only considers local effects. The interactions of all regions that considered both were more balanced, suggesting that spatial interactions among regions promoted the supplies of ES and the progress of SDGs. (2) The midstream of HRB formed a stronger spatial connectivity network. (3) Land use intensity, population density, and water scarcity showed a strong influence on the interactions. This study provides targeted suggestions to guide sustainable development for better human well-being while maintaining ecosystem health for better environmental performance.

Neuromorphic antennal sensory system

Insect antennae facilitate the nuanced detection of vibrations and deflections, and the non-contact perception of magnetic or chemical stimuli, capabilities not found in mammalian skin. Here, we report a neuromorphic antennal sensory system that emulates the structural, functional, and neuronal characteristics of ant antennae. Our system comprises electronic antennae sensor with three-dimensional flexible structures that detects tactile and magnetic stimuli. The integration of artificial synaptic devices adsorbed with solution-processable MoS2 nanoflakes enables synaptic processing of sensory information. By emulating the architecture of receptor-neuron pathway, our system realizes hardware-level, spatiotemporal perception of tactile contact, surface pattern, and magnetic field (detection limits: 1.3 mN, 50 μm, 9.4 mT). Vibrotactile-perception tasks involving profile and texture classifications were accomplished with high accuracy (> 90%), surpassing human performance in “blind” tactile explorations. Magneto-perception tasks including magnetic navigation and touchless interaction were successfully completed. Our work represents a milestone for neuromorphic sensory systems and biomimetic perceptual intelligence.

Dorsolateral prefrontal activity supports a cognitive space organization of cognitive control.

Cognitive control resolves conflicts between task-relevant and -irrelevant information to enable goal-directed behavior. As conflicts can arise from different sources (e.g., sensory input, internal representations), how a limited set of cognitive control processes can effectively address diverse conflicts remains a major challenge. Based on the cognitive space theory, different conflicts can be parameterized and represented as distinct points in a (low-dimensional) cognitive space, which can then be resolved by a limited set of cognitive control processes working along the dimensions. It leads to a hypothesis that conflicts similar in their sources are also represented similarly in the cognitive space. We designed a task with five types of conflicts that could be conceptually parameterized. Both human performance and fMRI activity patterns in the right dorsolateral prefrontal cortex support that different types of conflicts are organized based on their similarity, thus suggesting cognitive space as a principle for representing conflicts.

Dorsolateral prefrontal activity supports a cognitive space organization of cognitive control

Cognitive control resolves conflicts between task-relevant and -irrelevant information to enable goal-directed behavior. As conflicts can arise from different sources (e.g., sensory input, internal representations), how a limited set of cognitive control processes can effectively address diverse conflicts remains a major challenge. Based on the cognitive space theory, different conflicts can be parameterized and represented as distinct points in a (low-dimensional) cognitive space, which can then be resolved by a limited set of cognitive control processes working along the dimensions. It leads to a hypothesis that conflicts similar in their sources are also represented similarly in the cognitive space. We designed a task with five types of conflicts that could be conceptually parameterized. Both human performance and fMRI activity patterns in the right dorsolateral prefrontal cortex support that different types of conflicts are organized based on their similarity, thus suggesting cognitive space as a principle for representing conflicts.

Practice With Less AI Makes Perfect: Partially Automated AI During Training Leads to Better Worker Motivation, Engagement, and Skill Acquisition

The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human–AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators’ technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology.

The effects of mental fatigue on fine motor performance in humans and its neural network connectivity mechanism: a dart throwing study.

While it is well known that mental fatigue impairs fine motor performance, the investigation into its neural basis remains scant. Here, we investigate the impact of mental fatigue on fine motor performance and explore its underlying neural network connectivity mechanisms. A total of 24 healthy male university students were recruited and randomly divided into two groups: a mental fatigue group (MF) and a control group (Control). Both groups completed 50 dart throws, while electroencephalography (EEG) data were collected. Following the Stroop intervention, participants in the MF group exhibited a decrease in Stroop task accuracy and throwing performance, and an increase in reaction time along with VAS and NASA scores. The EEG data during dart-throwing revealed that the network connectivity strength of theta oscillations in the frontal and left central regions was significantly higher in the MF group compared with the Control group, while the network connectivity strength of alpha oscillations in the left parietal region was significantly enhanced. The interregional connectivity within the theta and alpha rhythm bands, particularly in the frontal-central-parietal network connections, also showed a significant increase in the MF group. Mental fatigue impairs dart throwing performance and is accompanied by increased connectivity in alpha and theta.

Operational pressure and the SHP framework: Connecting the dots for safety

Operational pressure refers to pressure, whether induced or self-induced, that can affect the (flight) operation and may also impact safety. It is a hot topic in many sectors, being an important reason for multiple personnel strikes - the aviation sector is no exception here. Given the rapid evolution of these different operational pressures as the world adapts, continuous attention will be necessary for addressing the concept of operational pressure and discovering new mitigation strategies. Therefore, this paper delves deeper into both current and future factors influencing operational pressure, as well as mentioning some potential mitigating measures. Moreover, the case of operational pressure is structured and visualised using NLR’s Safety & Human Performance (SHP) framework. The result provides a structured overview of the impacts of different operational pressures on safety, which can aid in developing mitigations to lessen the effect of such pressures.

A step-by-step guide to include key electroencephalography (EEG) parameters in the study of human performance applied to air traffic control

The study of human performance of air traffic controllers (ATCOs) is an interesting line of research to improve operational safety. In recent years, there has been an increase in the number of techniques available to develop this research based on massive data analysis. This study presents the use of certain electroencephalography (EEG) parameters to study the human performance of ATCOs. Although software and applications are now available to calculate these parameters, there are often problems in understanding the detailed process used to calculate them. The parameters presented in this study are intended to overcome this limitation and are applicable in real air traffic control (ATC) situations. Six parameters are analysed: excitement, stress, relaxation, boredom, engagement, and attention. As an application case for the parameters, a total of 50 data samples obtained during the development of real-time simulations on a highly realistic ATC platform are analysed. From these data, the above-mentioned EEG parameters and their trends are calculated. In addition, the evolution of these parameters is studied in relation to two other variables that characterise the operational situation of the sector during the simulations: the taskload based on ATC events and the number of simultaneous aircraft in the sector per minute.

Uniform vs. uneven lighting: Accommodation of multiple users’ preferences in study rooms

Uniform lighting in public spaces like study rooms in libraries, is still a common practice. However, uniformly lit spaces are unable to simultaneously meet the needs of multiple users and can create dull environments. The objective of this research is to analyze users’ behavior under different lighting conditions in study rooms and investigate whether alternative lighting designs, which step away from uniformity, could be more beneficial than uniform lighting, from a human performance and well-being perspective. A uniformly lit study room in the library of KTH was chosen as the main case study of this investigation and was thoroughly analyzed and evaluated through qualitative and quantitative research. Under the initial hypothesis that the lighting in this study room does not meet its users’ needs and fails to create an inspiring and comfortable environment, three additional study rooms in different Swedish libraries were comparatively analyzed through observations, interviews, and questionnaires, so as to better understand individual users’ needs and lighting preferences in study rooms. The research revealed the existence of various types of users with different personal preferences regarding lighting in order to feel comfortable and alert. A new conceptual lighting proposal is presented for the study room in KTH library, following an uneven lighting approach that utilizes different lighting zones to accommodate individual needs and affect the sense of comfort, alertness, and ultimately, the well-being of multiple types of users.

Exploring Simulation Workflows, Tools, and Metrics for Beyond-Vision Effects in Multi-Objective Optimization: A Scoping Review

Lighting in the built environment affects different aspects, ranging from building performance in terms of costs and energy consumption to human well-being in terms of thermal comfort, visual effects, and beyond-vision effects. Buildings optimised for specific performance metrics rarely consider different aspects simultaneously, leading to sub-optimised, unbalanced, or non-trade-off solutions. Therefore, multi-objective optimisation has commonly been used to overcome conflicting performance objectives. Recently, light effects beyond vision gained more interest in building design but it is unclear if and how they are integrated with other existing building performance metrics and simulation workflows. A scoping review investigated the state-of-art in multi-objective lighting design optimisation regarding building performance and human well-being focusing on performance metrics, simulation workflows, and the overall information flow. Results show that metrics for beyond-vision effects are not integrated with other human well-being and building performance metrics. The simulation workflows included multiple steps and computational tools in multi-objective optimisation. This process has limitations such as a long simulation time, no ubiquitous integrated tool, and a reduced information flow.

Implementasi Perilaku Kewarganegaraan Sebagai Mediasi Pengaruh Keterikatan Pegawai, Efikasi Diri Terhadap Kinearj Karyawan Pada PT Application Solutions

<p><em>Human resources are an important asset in a company, because human resources determine the success of a company. Human resource management and improving human resource performance have become the main focus of every company to encourage competitiveness in the future. A high-performing employee can make a significant contribution to achieving organizational goals. Currently, many companies have paid attention to factors to improve employee performance, including employee engagement and employee self-efficacy. This research aims to analyze the influence of employee engagement (X1) and self-efficacy (X2) through organizational citizenship behavior (OCB) (Z) as a mediating variable on the performance (Y) of PT Application Solutions employees. The research population included 115 employees, and the sampling technique used was a saturated sample, involving the entire population of PT Application Solutions. Data analysis in this research was carried out using the Partial Least Square (PLS) method with the support of SmartPLS 4 software. The findings indicate that, although the employee engagement variable has a positive influence, it does not reach a level of significance on employee performance. Additionally, employee engagement has a positive and significant influence on Organizational Citizenship Behavior (OCB). Furthermore, the self-efficacy variable has a positive and significant influence on employee performance and OCB. There is a positive and significant influence of the OCB variable on employee performance. Moreover, the OCB variable succeeded in being an effective mediator, connecting employee engagement and self-efficacy to employee performance, providing a positive and significant impact.</em></p>