Teamwork matters: team situation awareness to build high-performing healthcare teams, a narrative review.

Healthcare today is the prerogative of teams rather than of individuals. In acute care domains such as anaesthesia, intensive care, and emergency medicine, the work is complex and fast-paced, and the team members are diverse and interdependent. Three decades of research into the behaviours of high-performing teams provides us with clear guidance on team training, demonstrating positive effects on patient safety and staff wellbeing. Here we consider team performance through the lens of situation awareness. Maintaining situation awareness is an absolute requirement for safe and effective patient management. Situation awareness is a dynamic process of perceiving cues in the environment, understanding what they mean, and predicting how the situation may evolve. In the context of acute clinical care, situation awareness can be improved if the whole team actively contributes to monitoring the environment, processing information, and planning next steps. In this narrative review, we explore the concept of situation awareness at the level of the team, the conditions required to maintain team situation awareness, and the relationship between team situation awareness, shared mental models, and team performance. Our ultimate goal is to help clinicians create the conditions required for high-functioning teams, and ultimately improve the safety of clinical care.

Video analysis of real-life shoulder dystocia to assess technical and non-technical performance.

INTRODUCTION: Managing obstetric shoulder dystocia requires swift action using correct maneuvers. However, knowledge of obstetric teams' performance during management of real-life shoulder dystocia is limited, and the impact of non-technical skills has not been adequately evaluated. We aimed to analyze videos of teams managing real-life shoulder dystocia to identify clinical challenges associated with correct management and particular non-technical skills correlated with high technical performance. MATERIAL AND METHODS: We included 17 videos depicting teams managing shoulder dystocia in two Danish delivery wards, where deliveries were initially handled by midwives, and consultants were available for complications. Delivery rooms contained two or three cameras activated by Bluetooth upon obstetrician entry. Videos were captured 5 min before and after activation. Two obstetricians assessed the videos; technical performances were scored as low (0-59), average (60-84), or high (85-100). Two other assessors evaluated non-technical skills using the Global Assessment of Team Performance checklist, scoring 6 (poor) to 30 (excellent). We used a spline regression model to explore associations between these two score sets. Inter-rater agreement was assessed using interclass correlation coefficients. RESULTS: Interclass correlation coefficients were 0.71 (95% confidence interval 0.23-0.89) and 0.82 (95% confidence interval 0.52-0.94) for clinical and non-technical performances, respectively. Two teams had low technical performance scores; four teams achieved high scores. Teams adhered well to guidelines, demonstrating limited head traction, McRoberts maneuver, and internal rotation maneuvers. Several clinical skills posed challenges, notably recognizing shoulder impaction, applying suprapubic pressure, and discouraging women from pushing. Two non-technical skills were associated with high technical performance: effective patient communication, with teams calming the mother and guiding her collaboration during internal rotational maneuvers, and situation awareness, where teams promptly mobilized all essential personnel (senior midwife, consultant, pediatric team). Team communication, stress management, and task management skills were not associated with high technical performance. CONCLUSIONS: Videos capturing teams managing real-life shoulder dystocia are an effective tool to reveal challenges with certain technical and non-technical skills. Teams with high technical performance are associated with effective patient communication and situational awareness. Future training should include technical skills and non-technical skills, patient communication, and situation awareness.

Identifying intraoperative events in a simulated laparotomy video: a multinational study of inattentional blindness among anesthesiologists.

PURPOSE: Medical errors may be occasionally explained by inattentional blindness (IB), i.e., failing to notice an event/object that is in plain sight. We aimed to determine whether age/experience, restfulness/fatigue, and previous exposure to simulation education may affect IB in the anesthetic/surgical setting. METHODS: In this multicentre/multinational study, a convenience sample of 280 anesthesiologists watched an attention-demanding video of a simulated trauma patient undergoing laparotomy and (independently/anonymously) recorded the abnormalities they noticed. The video contained four expected/common abnormalities (hypotension, tachycardia, hypoxia, hypothermia) and two prominently displayed unexpected/rare events (patient's head movement, leaky central venous line). We analyzed the participants' ability to notice the expected/unexpected events (primary outcome) and the proportion of expected/unexpected events according to age group and prior exposure to simulation education (secondary outcomes). RESULTS: Anesthesiologists across all ages noticed fewer unexpected/rare events than expected/common ones. Overall, younger anesthesiologists missed fewer common events than older participants did (P = 0.02). There was no consistent association between age and perception of unexpected/rare events (P = 0.28), although the youngest cohort (< 30 yr) outperformed the other age groups. Prior simulation education did not affect the proportion of misses for the unexpected/rare events but was associated with fewer misses for the expected/common events. Self-perceived restfulness did not impact perception of events. CONCLUSION: Anesthesiologists noticed fewer unexpected/rare clinical events than expected/common ones in an attention-demanding video of a simulated trauma patient, in keeping with IB. Prior simulation training was associated with an improved ability to notice anticipated/expected events, but did not reduce IB. Our findings may have implications for understanding medical mishaps, and efforts to improve situational awareness, especially in acute perioperative and critical care settings.

RéSUMé: OBJECTIF: Les erreurs médicales peuvent parfois s'expliquer par la cécité d'inattention, soit le fait de ne pas remarquer un événement/objet qui est à la vue de tous et toutes. Notre objectif était de déterminer si l'âge/l'expérience, le repos/la fatigue et l'exposition antérieure à l'enseignement par simulation pouvaient affecter la cécité d'inattention dans le cadre de l'anesthésie/chirurgie. MéTHODE: Dans cette étude multicentrique/multinationale, un échantillon de convenance de 280 anesthésiologistes ont visionné une vidéo exigeant l'attention portant sur un patient de trauma simulé bénéficiant d'une laparotomie et ont enregistré (de manière indépendante/anonyme) les anomalies qu'ils et elles ont remarquées. La vidéo contenait quatre anomalies attendues/courantes (hypotension, tachycardie, hypoxie, hypothermie) et deux événements inattendus/rares bien en vue (mouvement de la tête du patient, fuite du cathéter veineux central). Nous avons analysé la capacité des participant·es à remarquer les événements attendus/inattendus (critère d'évaluation principal) et la proportion d'événements attendus/inattendus selon le groupe d'âge et l'exposition antérieure à l'enseignement par simulation (critères d'évaluation secondaires). RéSULTATS: Les anesthésiologistes de tous âges ont remarqué moins d'événements inattendus/rares que d'événements attendus/courants. Globalement, les anesthésiologistes plus jeunes ont manqué moins d'événements courants que leurs congénères plus âgé·es (P = 0,02). Il n'y avait pas d'association constante entre l'âge et la perception d'événements inattendus ou rares (P = 0,28), bien que la cohorte la plus jeune (< 30 ans) ait surpassé les autres groupes d'âge. La formation antérieure par simulation n'a pas eu d'incidence sur la proportion d'inobservation des événements inattendus ou rares, mais a été associée à moins de cécité d'inattention envers les événements attendus ou courants. Le repos perçu n'a pas eu d'impact sur la perception des événements. CONCLUSION: Les anesthésiologistes ont remarqué moins d'événements cliniques inattendus/rares que d'événements attendus/courants dans une vidéo exigeant l'attention portant sur la simulation d'un patient traumatisé, ce qui s'inscrit dans la cécité d'inattention. La formation préalable par simulation était associée à une meilleure capacité à remarquer les événements anticipés/attendus, mais ne réduisait pas la cécité d'inattention. Nos résultats peuvent avoir des implications pour la compréhension des accidents médicaux et les efforts visant à améliorer la conscience situationnelle, en particulier dans les contextes de soins périopératoires aigus et de soins intensifs.

Spectrum Situation Awareness for Space-Air-Ground Integrated Networks Based on Tensor Computing.

The spectrum situation awareness problem in space-air-ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space-air-ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.

Assessment of Surgeons' Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study.

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human-robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

Energy Consumption Displays in Electric Vehicles: Differential Effects on Estimating Consumption and Experienced Energy Dynamics Awareness.

OBJECTIVE: The effects of three prototypical designs of energy consumption displays on energy-specific situation awareness were examined. BACKGROUND: Energy efficiency is crucial for the sustainability of technical systems. However, without accurate situation awareness of energy dynamics (energy dynamics awareness, EDA) it can be challenging for humans to optimize the use of energy resources of electric vehicles (EVs) through their behavior. METHOD: We examined three prototypical energy display designs that varied by their informational value to support EDA. Furthermore, we investigated the differential effects on EDA measured by (1) a newly constructed scale (experienced EDA), (2) estimating energy consumption, and (3) identifying efficient trips in an online experiment. Participants (N = 82) watched standardized driving scenes (videos) of EV trips presenting the energy displays. RESULTS: We found a strong effect of display type on experienced EDA, with the trace display being the most supportive. The EDA scale showed excellent internal consistency. The consumption estimation and efficient trip identification indicators were not affected by the display type. CONCLUSION: The study indicates that experienced EDA is immediately affected by displays with higher information value, but performance might need more time and training. More research is needed to investigate the cognitive processes related to EDA and to examine how distinct display elements enhance EDA. APPLICATION: Results from this research can be used as guidance for the design of energy displays, especially in EVs. The EDA scale can be used as an evaluation measure in the human-centered design process of energy displays.

Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies.

OBJECTIVE: An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies. BACKGROUND: Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking. METHOD: A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion. RESULTS: A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers. CONCLUSION: The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs. APPLICATION: This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

High-speed train drivers' human error under fatigue and stress: the role of situation awareness and individual differences.

Fatigue and stress are critical variables that impair railway train drivers' safety performance, and individual differences may influence these effects. This study investigates how fatigue and stress affect high-speed train drivers' human error and the role of individual differences. We hypothesised that situation awareness (SA) mediates the effects of fatigue and stress on human error, and individual differences (age and work experience) moderate these effects. We surveyed 1,391 male drivers from eight Chinese railway bureaus and used PROCESS Macro for data analysis. The results revealed that fatigue and stress increased human error, directly and indirectly through SA. Age and work experience moderated the effect of fatigue and stress on SA, respectively. Older drivers had better SA under high fatigue, while more experienced drivers had better SA under high stress. These findings can inform more tailored safety management strategies to lower human error and enhance the safety of high-speed train operations.

A cross-sectional survey of 1,391 high-speed train drivers in China indicated that fatigue and stress amplify human error by impairing situation awareness (SA). Age and work experience were observed to moderate the impact of fatigue and stress on SA, respectively. These insights guide the advancement of safety management strategies.

Automation function and malfunction: effects on human performance in accident handling tasks.

By conducting a mixed-design experiment using simplified accident handling tasks performed by two-person teams, this study examined the effects of automation function and condition (before, during, and after malfunction) on human performance. Five different and non-overlapping functions related to human information processing model were considered and their malfunctions were set in a first-failure way. The results showed that while the automation malfunction impaired task performance, the performance degradation for information analysis was more severe than response planning. Contrary to other functions, the situation awareness for response planning and response implementation tended to increase during malfunctioning and decrease after. In addition, decreased task performance reduced trust in automation, and malfunctions in earlier stages of information processing resulted in lower trust. Suggestions provided for the design and training related to automation emphasise the importance of high-level cognitive support and the benefit of involving automation error handling in training.

The effects of automation function and malfunction on human performance are important for design and training. The experimental results in this study revealed the significance of high-level cognitive support. Also, introducing automation error handling in training can be helpful in improving situation awareness of the teams.

EEG and fNIRS are associated with situation awareness (hazard) prediction during a driving task.

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task.

We examine whether neurophysiological sensors are useful to objectively measure Level 3 situation awareness (SA) prediction during a driving task. EEG theta and beta, and fNIRS oxygenated haemoglobin were most sensitive to SA accuracy. This is evidence that neurophysiological measures can be used to assess hazard prediction (Level 3 SA).

Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness.

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly. Subject variability is considered as a domain shift problem. Several attempts have been made to find domain-invariant features among subjects, where subject-specific information is neglected. In this work, we propose a simple but efficient subject matching framework by finding a connection between a target (test) subject and source (training) subjects. Specifically, the framework includes two stages: (1) we train the model with multi-source domain alignment layers to collect source domain statistics. (2) During testing, a distance is computed to perform subject matching in the latent representation space. We use a reciprocal exponential function as a similarity measure to dynamically select similar source subjects. Experiment results show that our framework achieves a state-of-the-art accuracy 74.32% for the Taiwan driving dataset.

Human-centered visualization technologies for patient monitoring are the future: a narrative review.

Medical technology innovation has improved patient monitoring in perioperative and intensive care medicine and continuous improvement in the technology is now a central focus in this field. Because data density increases with the number of parameters captured by patient-monitoring devices, its interpretation has become more challenging. Therefore, it is necessary to support clinicians in managing information overload while improving their awareness and understanding about the patient's health status. Patient monitoring has almost exclusively operated on the single-sensor-single-indicator principle-a technology-centered way of presenting data in which specific parameters are measured and displayed individually as separate numbers and waves. An alternative is user-centered medical visualization technology, which integrates multiple pieces of information (e.g., vital signs), derived from multiple sensors into a single indicator-an avatar-based visualization-that is a meaningful representation of the real-world situation. Data are presented as changing shapes, colors, and animation frequencies, which can be perceived, integrated, and interpreted much more efficiently than other formats (e.g., numbers). The beneficial effects of these technologies have been confirmed in computer-based simulation studies; visualization technologies improved clinicians' situation awareness by helping them effectively perceive and verbalize the underlying medical issue, while improving diagnostic confidence and reducing workload. This review presents an overview of the scientific results and the evidence for the validity of these technologies.

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch.

Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

Survey of Technology in Network Security Situation Awareness.

Network security situation awareness (NSSA) is an integral part of cybersecurity defense, and it is essential for cybersecurity managers to respond to increasingly sophisticated cyber threats. Different from traditional security measures, NSSA can identify the behavior of various activities in the network and conduct intent understanding and impact assessment from a macro perspective so as to provide reasonable decision support, predicting the development trend of network security. It is a means to analyze the network security quantitatively. Although NSSA has received extensive attention and exploration, there is a lack of comprehensive reviews of the related technologies. This paper presents a state-of-the-art study on NSSA that can help bridge the current research status and future large-scale application. First, the paper provides a concise introduction to NSSA, highlighting its development process. Then, the paper focuses on the research progress of key technologies in recent years. We further discuss the classic use cases of NSSA. Finally, the survey details various challenges and potential research directions related to NSSA.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness.

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles-A Survey.

Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.

Physiological Measurements of Situation Awareness: A Systematic Review.

OBJECTIVE: The goal of this systematic literature review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). BACKGROUND: Across different environments and tasks, assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SAGAT, SPAM, and/or SART. However, research suggests that indirect physiological sensing methods may also be capable of predicting SA. Currently, it is unclear which particular physiological approaches are sensitive to changes in SA. METHOD: Seven databases were searched using the PRISMA reporting guidelines. Eligibility criteria included human-subject experiments that used at least one direct SA assessment technique, as well as at least one physiological measurement. Information extracted from each article was the physiological metric(s), the direct SA measurement(s), the correlation between these two metrics, and the experimental task(s). All studies underwent a quality assessment. RESULTS: Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA were mixed. EEG studies were too few to form strong conclusions, but were consistently positive. CONCLUSION: Further investigation is needed to methodically collect more relevant data and comprehensively model the relationships between a wider range of physiological measurements and direct assessments of SA. APPLICATION: This review will guide researchers and practitioners in methods to indirectly assess SA with sensors and highlight opportunities for future research on wearables and SA.

An Eye-Fixation Related Electroencephalography Technique for Predicting Situation Awareness: Implications for Driver State Monitoring Systems.

OBJECTIVE: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. BACKGROUND: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. METHOD: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. RESULTS: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. CONCLUSION: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. APPLICATION: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving.

Comparison of Display Modality and Human-in-the-Loop Presence for On-Orbit Inspection of Spacecraft.

OBJECTIVE: To investigate the impact of interface display modalities and human-in-the-loop presence on the awareness, workload, performance, and user strategies of humans interacting with teleoperated robotic systems while conducting inspection tasks onboard spacecraft. BACKGROUND: Due to recent advancements in robotic technology, free-flying teleoperated robot inspectors are a viable alternative to extravehicular activity inspection operations. Teleoperation depends on the user's situation awareness; consequently, a key to successful operations is practical bi-directional communication between human and robot agents. METHOD: Participants (n = 19) performed telerobotic inspection of a virtual spacecraft during two degrees of temporal communication, a Synchronous Inspection task and an Asynchronous Inspection task. Participants executed the two tasks while using three distinct visual displays (2D, 3D, AR) and accompanying control systems. RESULTS: Anomaly detection performance was better during Synchronous Inspection than the Asynchronous Inspection of previously captured imagery. Users' detection accuracy reduced when given interactive exocentric 3D viewpoints to accompany the egocentric robot view. The results provide evidence that 3D projections, either demonstrated on a 2D interface or augmented reality hologram, do not affect the mean clearance violation time (local guidance performance), even though the subjects perceived a benefit. CONCLUSION: In the current implementation, the addition of augmented reality to a classical egocentric robot view for exterior inspection of spacecraft is unnecessary, as its margin of performance enhancement is limited in comparison. APPLICATION: Results are presented to inform future human-robot interfaces to support crew autonomy for deep space missions.

The Impact of Information Integration in a Simulation of Future Submarine Command and Control.

OBJECTIVE: Examine the extent to which increasing information integration across displays in a simulated submarine command and control room can reduce operator workload, improve operator situation awareness, and improve team performance. BACKGROUND: In control rooms, the volume and number of sources of information are increasing, with the potential to overwhelm operator cognitive capacity. It is proposed that by distributing information to maximize relevance to each operator role (increasing information integration), it is possible to not only reduce operator workload but also improve situation awareness and team performance. METHOD: Sixteen teams of six novice participants were trained to work together to combine data from multiple sensor displays to build a tactical picture of surrounding contacts at sea. The extent that data from one display were available to operators at other displays was manipulated (information integration) between teams. Team performance was assessed as the accuracy of the generated tactical picture. RESULTS: Teams built a more accurate tactical picture, and individual team members had better situation awareness and lower workload, when provided with high compared with low information integration. CONCLUSION: A human-centered design approach to integrating information in command and control settings can result in lower workload, and enhanced situation awareness and team performance. APPLICATION: The design of modern command and control rooms, in which operators must fuse increasing volumes of complex data from displays, may benefit from higher information integration based on a human-centered design philosophy, and a fundamental understanding of the cognitive work that is carried out by operators.