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Background: Spatial problem-solving is an essential skill for success in many engineering disciplines; thus, understanding the cognitive processes involved could help inform the design of training interventions for students trying to improve this skill. Prior research has yet to investigate the differences in cognitive processes between spatial tasks in problem-solving to offer learners timely feedback. Purpose/Hypothesis: In this study, we investigated how different spatial tasks change the cognitive processes and problem-solving strategies used by engineering students with low spatial ability. Design/Method: Study participants completed mental rotation and mental cutting tasks of high and low difficulty. Eye-tracking data were collected and categorized as encoding, transformation, and confirmation cognitive processes. The adoption of either a holistic or piecemeal strategy and response accuracy were also measured. Results: Mental rotation was found to have a higher number of fixations for each cognitive process than the mental cutting task. The holistic strategy was used in both difficulty levels of the mental cutting task, while the piecemeal strategy was adopted for the mental rotation task at a high difficulty level. Only encoding fixations were significantly correlated with accuracy and most strongly correlated with strategy. Conclusion: Encoding is an important cognitive process that could affect subsequent cognitive processes and strategies and could, thus, play an important role in performance. Future development in spatial training should consider how to enhance encoding to aid students with low spatial ability. Educators can utilize gaze metrics and empirical research to provide tailored and timely feedback to learners. Future of Work at the Human-Technology Frontier program of the National Science Foundation; [1839946] Published version ACKNOWLEDGMENT The authors thank the reviewers for their insightful feedback on our manuscript. This research is in part supported by the Future of Work at the Human-Technology Frontier program of the National Science Foundation (Award Number 1839946).

Eye metrics are effective indicators of focused visual attention and perceived workload that have been used to differentiate surgical expertise and task difficulties. However, the change in eye metrics throughout surgical training in a cohort of trainees is under-investigated. This study collected eye-tracking data from 13 medical students practicing the peg transfer task until reaching the passing criteria of the Fundamentals of Laparoscopic Surgery. Six eye metrics measuring focused visual attention and workload were computed and then used in multiple linear regression analysis to predict trial completion time. All predictors were significant in the regression model, collectively explaining 61.7% of the variance in log-transformed completion time. Fixation rates and gaze entropy were the most important metrics at revealing skill acquisition as medical students self-train on the peg-transfer task. The results on these eye metrics demonstrate potential in assessing surgeons-in-training and providing feedback to ensure surgical competency.

Supervisory Control and Data Acquisition (SCADA) systems are widely adopted in critical infrastructures and prime targets of cyberattacks. Ecological Interface Design (EID) is postulated to be an invaluable framework for supporting operators to cope with cyber intrusions, particularly zero-day attacks because prior research has demonstrated effectiveness of ecological interfaces during unanticipated events. However, a suitable research platform is absent for studying user interface in cybersecurity of SCADA systems. This paper presents a SCADA system simulation being designed and implemented for the DURESS thermohydraulic process control simulation common in EID studies. Based on the open literature and industrial standards to ensure representativeness of industrial SCADA systems, the simulation includes two programable logical controllers, seven routers, and a server in a wired communication network. These components should be sufficient to study human response to common cyberattacks on SCADA systems and support future work in prototyping and evaluating user interfaces for SCADA cybersecurity.

Spatial strategies and cognitive processes have been separately found to indicate performance in spatial problem-solving, but their relationships have not been investigated quantitatively. This study employed eye-tracking to characterize and determine the relationship of the cognitive processes and spatial strategies employed by first year engineering students with low spatial ability. Eighty-eight engineering students with low spatial ability were recruited to complete test items in the Purdue Spatial Visualization Test: Rotations (PSVT:R) and the Santa Barbara Solids Test (SBST). Eye-tracking data were collected to indicate the encoding, transformation, confirmation cognitive processes as well as whether holistic or piecemeal strategy was employed. Strategy ratio correlated with encoding fixations more than other cognitive processes. This finding suggests that encoding may be more indicative of spatial strategies being employed than the transform and confirmation processes. Pedagogical practices for low spatial ability students may turn to improving the encoding process for better learning outcomes.

This paper presents a novel cable-driven exoskeleton (BiEXO) for the upper limb including shoulder and elbow joints. BiEXO is made of carbon fiber that is inspired by the Bamboo structure. The key components of BiEXO are carbon fiber tubes that mimic bamboo tubes. A combined driver is developed for BiEXO with two cable-driven mechanisms (CDMs) and a power transmission belt (PTB). The CDMs are used for shoulder and elbow flexion/extension movement utilizing cables to mimic the skeletal muscles function, while the PTB system drives a shoulder link to mimic the scapula joint for shoulder abduction/adduction movement. Simulation studies and evaluation experiments were performed to demonstrate the efficacy of the overall system. To determine the strength-to-weight of the bamboo-inspired links and guarantee high buckling strength in the face of loads imposed from the user side to the structure, finite element analysis (FEA) was performed. The results show that the carbon fiber link inspired by bamboo has more strength in comparison to the common long carbon fiber tube. The kinematic configuration was modeled by the modified Denavit-Hartenberg (D-H) notation. The mean absolute error (MAE) was 5.9 mm, and the root-mean-square error (RMSE) was 6 mm. In addition, verification experiments by tracking the trajectory in Cartesian space and the wear trials on a subject were carried out on the BiEXO prototype. The satisfactory results indicate BiEXO to be a promising system for rehabilitation or assistance in the future.

Context dependent gaze metrics, derived from eye movements explicitly associated with how a task is being performed, are particularly useful for formative assessment that includes feedback on specific behavioral adjustments for skill acquisitions. In laparoscopic surgery, context dependent gaze metrics are under investigated and commonly derived by either qualitatively inspecting the videos frame by frame or mapping the fixations onto a static surgical task field. This study collected eye-tracking and video data from 13 trainees practicing the peg transfer task. Machine learning algorithms in computer vision were employed to derive metrics of tool speed, fixation rate on (moving or stationary) target objects, and fixation rate on tool-object combination. Preliminary results from a clustering analysis on the measurements from 499 practice trials indicated that the metrics were able to differentiate three skill levels amongst the trainees, suggesting high sensitivity and potential of context dependent gaze metrics for surgical assessment.

Surgical training includes the development of technical and nontechnical skills. While technical skills are more easily quantified, nontechnical skills such as situation awareness (SA) are more difficult to measure and quantify. This study investigated the relationships between different SA elements and expertise.Twenty attending and resident surgeons rated their anticipation of an impending adverse event while watching 20 videos of laparoscopic cholecystectomies with and without adverse events. After watching each video, they assessed surgeon skills and self-assessed their anticipation ratings. All participants answered a general confidence questionnaire before and after the study.Videos with adverse events led to significantly higher anticipation of adverse events (P 0.001), lower surgeon skill rating (P 0.001), and higher self-assessment in their anticipation ratings (P 0.001) across both participant groups. General confidence was significantly lower for residents than that for attending surgeons (P 0.001). Compared with the residents, attendings exhibited stronger and more stable correlations between measurements of SA. When viewing videos with adverse events, attendings showed significantly higher correlation between anticipation of an impending adverse event and skill assessment of the surgeon (P = 0.005).This study investigated how different elements of SA and their relationships were influenced by experience. The results indicated that attendings had stronger and more stable correlations between SA elements than residents, demonstrating how measurement correlations could be meaningful and sensitive indicators of expertise and autonomy readiness.

Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called P ersonalized R ecommender System for I nformation visualization M ethods via E xtended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users’ states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.

Full-body, powered wearable exoskeletons combine the capabilities of machines and humans to maximize productivity. Powered exoskeletons can ease industrial workers in manipulating heavy loads in a manner that is difficult to automate. However, introduction of exoskeletons may result in unexpected work hazards, due to the mismatch between user-intended and executed actions thereby creating difficulties in sensing the physical operational envelope, need for increased clearance, and maneuverability limitations. To control such hazards, this paper presents a rearview human localization augmented reality (AR) platform to enhance spatial awareness of people behind the exoskeleton users. This platform leverages a computer vision algorithm called Monocular 3D Pedestrian Localization and Uncertainty Estimation (MonoLoco) for identifying humans and estimating their distance from a video camera feed and off-the-shelf AR goggles for visualizing the surrounding. Augmenting rear view awareness of humans can help exoskeleton users to avoid accidental collisions that can lead to severe injuries.

Patient flow management is a system-wide process but many healthcare providers do not integrate multiple departments into the process to minimise the time between treatments or medical services for maximum patient throughput. This paper presents a case study of applying Distributed Situation Awareness (DSA) to characterise system-wide patient flow management and identify opportunities for improvements in a healthcare system. This case study employed a three-part method of data elicitation, extraction, and representation to investigate DSA. Social, task, and knowledge networks were developed and then combined to characterise patient flow management and identify deficiencies of the command and control centre of a healthcare facility. Social network analysis provided centrality metrics to further characterise patient flow management. The DSA model helped identify design principles and deficiencies in managing patient flow. These findings indicate that DSA is promising for analysing patient flow management from a system-wide perspective. Practitioner summary: This article examines Distribution Situation Awareness (DSA) as an analysis framework to study system-wide patient flow management. The DSA yields social, task, and knowledge networks that can be combined to characterise patient flow and identify deficiencies in the system. DSA appears promising for analysing communication and coordination of complex systems. Abbreviations: CDM: critical decision method; CTaC: carilion transfer and communications center; EAST: event analysis systematic teamwork; ED: emergency department; DES: discrete event simulation; DSA: distributed situation awareness; SA: situation awareness; SNA: social network analysis.

In laparoscopic surgery, looking in the target areas is an indicator of proficiency. However, gaze behaviors revealing feedforward control (i.e., looking ahead) and their importance have been under-investigated in surgery. This study aims to establish the sensitivity and relative importance of different scene-dependent gaze and motion metrics for estimating trainee proficiency levels in surgical skills.Medical students performed the Fundamentals of Laparoscopic Surgery peg transfer task while recording their gaze on the monitor and tool activities inside the trainer box. Using computer vision and fixation algorithms, five scene-dependent gaze metrics and one tool speed metric were computed for 499 practice trials. Cluster analysis on the six metrics was used to group the trials into different clusters/proficiency levels, and ANOVAs were conducted to test differences between proficiency levels. A Random Forest model was trained to study metric importance at predicting proficiency levels.Three clusters were identified, corresponding to three proficiency levels. The correspondence between the clusters and proficiency levels was confirmed by differences between completion times (FScene-dependent gaze metrics revealed skill levels of trainees more precisely than between experts and novices as suggested in the literature. Further, feedforward gaze metrics appeared to be more important than feedback ones at predicting proficiency.

Substance use disorders present major health risks and economic burdens, while current healthcare systems face insufficient resources and accessibility deficiencies that challenge long term treatment for chronic diseases. This paper presents a theoretical approach of integrating the concept of episodic future thinking into immersive virtual reality to treat substance use disorders. Episodic future thinking can extend the temporal window over which an individual values rewards, rebalancing the impulsive and executive decision systems and thereby reducing the valuation of substances of abuse. In theory, virtual reality can enhance episodic future thinking by providing realistic cues and contexts. We propose using schema theory to guide the design of virtual reality for facilitating episodic future thinking. Future work should focus on developing and evaluating a proof-of-concept of this theoretical framework.

New human-centered sensing capabilities are introduced in surface transportation, aviation, healthcare, as well as many other professional and recreational domains. These capabilities enable industrial systems and consumer products to monitor a variety of human states and behaviors, enabling machines to better understand the users and to provide sophisticated, natural user interactions, decision aiding, and behavioral guidance. This panel invites five researchers to discuss what the Human Factors community can contribute to advancing human-centered sensing, and what technical and ethical challenges can arise in research and applications.

Objective With recent changes to graduate medical education, the balance between resident autonomy and need for supervision impacts the educational and training experience of residents. The objective of this study was to understand the relationship between the confidence of attendings and residents and their different perspectives of perceived educational experience and autonomy in the operating room (OR). We hypothesized that the attending's confidence in the resident would be an important factor in improving the educational experience and resident's autonomy in the OR. Design Self-reported confidence-rating and operative experience surveys were administered to teams of post-graduate year (PGY 1) through PGY 5 surgical residents and attendings in two temporal sets (Early: Sept-Dec 2015, n = 20; Late: Jan-Apr 2016, n = 22). A second “end-of-year” survey was distributed to residents (n = 9, 37.5% response) and attendings (n = 10, 35% response) asking questions regarding their educational experience and operative experience during the past year. Setting Large rural teaching hospital. Participants Nineteen general surgery residents (PGY 1 - 5) and 14 general surgery attendings. Results Resident perception of confidence differs from junior to senior residents, and that there was discordance between resident's confidence and skill as perceived by attendings, particularly in senior residents. Results also showed that attending's confidence in residents was positively correlated with attending's perceived educational experience in the OR. Residents and attendings both indicated attending's confidence in residents as an important factor in increasing resident autonomy in the OR, thus the attending's confidence in residents could have a positive impact on resident autonomy and educational experience in the OR. Conclusions We have demonstrated a relationship between self-confidence for residents and improved confidence from attendings in residents’ capabilities. Based on these findings, we would propose identifying methods to expand resident's awareness of surgical situations and develop attending's confidence in residents.

Provisionary command centers for containing disease outbreaks can be an effective strategy in coordinating efforts by different government ministries and agencies to provide resilience that limits human and economic losses. This paper applies the Distributed Situation Awareness (DSA) framework to investigate the fundamental design and operational principles of provisionary command centers that contribute to the resilience against disease outbreaks. The investigation involves a case study on modeling the DSA of the Taiwan Central Epidemic Command Center (CECC) that coordinates multiple government ministries and agencies to contain COVID-19. Task, knowledge, and social networks were developed and combined into one DSA network model to depict how the CECC operations in processing and distributing information enhance situation awareness (SA) of all the government agencies and citizens for a coordinated response to the pandemic. The DSA network model illustrates an interorganizational system architecture that can enable graceful extensibility of various government functions and sustained adaptability of the agencies against disease outbreaks.

Background and Objectives The increasing number of senior drivers may introduce new road risks due to age-related declines in physical and cognitive abilities. Advanced driver assistance systems (ADAS) have been proposed as solutions to minimize age-related declines, thereby increasing both senior safety and mobility. This study examined factors that influence seniors’ attitudes toward adopting ADAS after significant exposure to the technology in naturalistic settings. Research Design and Methods This study recruited 18 senior drivers aged 70–79 to drive vehicles equipped with ADAS for 6 weeks in their own environments. Afterward, each participant was enrolled in 1 of the 3 focus group sessions to discuss their changes in attitude toward ADAS based on their driving experiences. We applied structural topic modeling (STM) on the focus group transcripts to reveal key topics deemed important to seniors. Results STM revealed 5 topics of importance for seniors. In order of prevalence, these were (i) safety, (ii) confidence concerning ADAS, (iii) ADAS functionality, (iv) user interface/usability, and (v) non-ADAS–related features. Based on topics and associated keywords, seniors perceived safety improvement with ADAS but expressed concerns about its limitations in coping with adverse driving conditions. Experience and training were suggested for improving seniors’ confidence in ADAS. Blind spot alert and adaptive cruise control received the most discussion regarding perceived safety and comfort. Discussion and Implications This study indicated that promoting road safety for senior drivers through ADAS is feasible. Acceptance and appropriate use of ADAS may be supported through intuitive and senior-friendly user interfaces, in-depth training programs, and owner’s manuals specifically designed and tested for senior drivers.

As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training. This panel invites five researchers in different domains to discuss how human factors can contribute to machine learning research and applications, as well as how machine learning presents both challenges and contributions for human factors.

Managing patient flow can be an effective strategy to reduce idling hospital beds, thereby lowering the healthcare cost without sacrificing quality of care. However, improving patient flow can be a major challenge due to the complex patterns of communication across diverse hospital staff. To identify improvement opportunities, this paper investigates whether the Distributed Situation Awareness (DSA) framework can feasibly and meaningfully model Situation Awareness (SA) in patient flow. The investigation involved a case study on modeling the DSA of the admission phase in patient flow for a level 1 trauma center. A DSA model combining task, knowledge, and social networks was created, showing feasibility of the framework in depicting the distribution and transaction of knowledge across workers and information systems. Further, a true elective admission case was mapped onto the DSA model, verifying its practical merits.

Today, cybersecurity is impacting every individual and industry, but the level of effort from the human factors community seems negligible compared to the magnitude of the current security challenge. This panel invites professionals in the healthcare, computer and network, higher education, and automotive industries to share experiences, lessons learned and solutions. The panel characterizes the security landscape in different industries and facilitate discussion on human factors research and applications to address the formidable security challenge.

Physiological data collection methods are unobtrusive, passive, continuous, and objective. The information afforded by sensors collecting physiological data can be transformed to represent operator performance estimates and stress state visualizations in real time. This technology is conducive to healthcare settings, creating the potential to inform healthcare operators of their current performance and physiological statuses. Despite the broad and pervasive utility of sensor technology, its applications in healthcare are underutilized and misunderstood. This is likely due to the combination of a lack of understanding of the full capabilities of sensor technology, a scarcity of demonstrated uses in healthcare, and an uncertainty surrounding translation and implementation into practice. Implementing findings from providers’ physiological data can be met with challenges, especially in the healthcare setting. Clinicians are most frequently concerned with patient care, and may not always recognize the importance of their own physiological state. While transitioning sensor technology from personal monitoring purposes to a data collection tool can be challenging, passive data collection via sensor technology may have significant value for learners and experienced practitioners. The goals of this paper are to: 1. raise awareness of sensor technology and its utility in clinical settings; 2. provide empirical examples of how to use sensor technology to answer basic and applied questions pertaining to clinical workflow; and 3. exemplify scalability and translatability of findings from sensor technology studies in clinical settings.

Continuous, non-invasive workload indicators of operators is an essential component for dynamically assigning appropriate amount of tasks between automation and operators to prevent overload and out-of-the-loop problems in computer-based procedures. This article examines the monitoring task difficulty manipulated by the task type and load, and explores physiological measurements in relation to mental workload. In a within-subject design experiment, forty-five university students performed monitoring tasks in simulated nuclear power plants (NPPs) control room. The performance of monitoring tasks (accuracy), subjective mental workload (NASA Task Load Index), as well as four eye-related physiological indices were measured and analyzed. The results show that as monitoring task difficulty increased, task performance significantly decreased while NASA-TLX, number of fixations, and dwell time significantly increased. Number of fixations and dwell time could be effective, non-invasive continuous indicators of workload for enabling adaptive computer-based procedures.

Cyber threats are targeting vulnerabilities of human workers performing tasks in manufacturing processes, including visual inspection to bias their decision-making, thereby sabotaging product quality. This article examines the use of priming as a form of "cognitive hacking" to adversely affect quality inspection decisions in manufacturing, and investigates physiological measurements as means to detect such intrusion. In a within-subject design experiment, twenty participants inspected surface roughness of a manufactured component with and without exposure to priming on the display of an inspection logging system. The results show that the presence of primes impacted accuracy on surface roughness, cortical activities at parietal lobe P4, and eye gaze for inspecting components. The experiment provides supporting evidence that basic hacking of a worker display can be an effective method to alter decision making in inspection. The findings also illustrate that cortical activities and eye gaze can be useful indicators of cognitive hacking. A major implication of the study results is that physiological indicators can be effective at revealing unconscious cognitive influence in visual inspection.

Eye-gaze metrics derived from areas of interest (AOIs) have been suggested to be effective for surgical skill assessment. However, prior research is mostly based on static images and simulated tasks that may not translate to complex and dynamic surgical scenes. Therefore, eye-gaze metrics must advance to account for changes in the location of important information during a surgical procedure. We developed a dynamic AOI generation technique based on eye gaze collected from an expert viewing surgery videos. This AOI updated as the gaze of the expert moved with changes in the surgical scene. This technique was evaluated through an experiment recruiting a total of 20 attendings and residents to view 10 videos associated with and another 10 without adverse events. Dwell time percentage (i.e., gaze duration) inside the AOI differentiated video type (U = 13508.5, p

Study objectives This study investigated the association between driving safety and seven sleep disorders amongst 3541 participants of the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study. Methods SHRP 2 collected naturalistic driving data from participants between 16 and 98 years old by instrumenting participants' vehicles. The analyses used logistic regression to determine the likelihood of crash or near-crash involvement, Poisson log-linear regression to assess crash or near-crash rate, and ordinal logistic regression to assess driver maneuver appropriateness and crash or near-crash severity. These analyses did not account for any medical treatments for the sleep disorders. Results Females with restless legs syndrome/Willis-Ekbom disease (RLS/WED), drivers with insomnia or narcolepsy, are associated with significantly higher risk of crash or near-crash. Drivers with shift work sleep disorder (SWSD) are associated with significantly increased crash or near-crash rate. Females with RLS/WED or sleep apnea and drivers with SWSD are associated with less safe driver maneuver and drivers with periodic limb movement disorder are associated with more severe events. The four analyses provide no evidence of safety decrements associated with migraine. Conclusions This study is the first examination on the association between seven sleep disorders and different measures of driving risk using large-scale naturalistic driving study data. The results corroborate much of the existing simulator and epidemiological research related to sleep-disorder patients and their driving safety, but add ecological validity to those findings. These results contribute to the empirical basis for medical professionals, policy makers, and employers in making decisions to aid individuals with sleep disorders in balancing safety and personal mobility.