bluuebunny/crossref_metadata_2025 huggingface dataset:
Real Life Does Not Always Get in the Way: Verbal Memory and the Sustained Attention to Response Task
Authors: Smith Samantha L. , Edgar Graham K. , Russell Paul N. , Helton William S.
Published: February 2021

The Sustained Attention to Response Task (SART) is a go/no-go task where participants must respond frequently to target stimuli and withhold responses from infrequent neutral stimuli. Researchers have shown that the fast and frequent responding characteristic of SART is typically associated with difficulty withholding responses to no-go stimuli. Imposing additional cognitive demands has been shown to further impair task performance. In the present research, participants completed a modified SART task, a narrative memory task, and a dual-task condition where both were done simultaneously. No significant performance impairments were found in the dual- compared to single-task conditions. The tasks’ non-overlapping resource demands, alongside a potential arousing benefit of the memory task, may explain the lack of notable dual-task interference. Future research is needed to better understand the effects of arousal and other factors that may help to uncouple errors of commission from response time, particularly in tasks with high ecological validity.
An Adaptive Fuzzy Modeling of Visual Attention in Real-world Interaction with Health Information System
Authors: Sridhar Smriti , Kwon MD. Younghoon , Cho MD. Yeilim , Kim PhD. Inki
Published: February 2021

Bottom-up and top-down processes are the two mechanisms of visual attention allocation, which allow people to efficiently spot task-relevant stimuli from cluttered and noisy environments, while staying alert to abnormalities within the visual field of view. This paper presents a preliminary study of the physicians’ real-life interaction with Information Communication Technology (ICT) in their own offices, along with extensively analyzing one case of an hour-long interaction of a physician, in which one performs a daily routine of reviewing patient electronic health records (EHRs) and writing diagnostic notes to the system interface. The physician interactions were captured in a time series data by recording display screen, keystrokes and mouse movements, also by simultaneously tracking eye movements. Then, a fuzzy-based model that can distinguish bottom-up and top-down processes were defined by using statistical random variables in terms of eye-movement patterns. The shift between those two attentional processes was detected by tracking the parametric changes of gaze behaviors as input: significant shift of fixation, sustained gazing, and fixation trajectory over time. Based on those gaze metrics, a random variable was assigned to the discrete probability of low (0), medium (0.5), or high (1.0), for a quantified fuzzy output, which was further machine-learned into an Adaptive Neuro-Fuzzy Inference System (ANFIS) model in order to judge how a physician is likely to be dominated by a bottom-up or top-down processes in performing a task at that instance in time. On training the ANFIS model with three different types of fuzzy membership functions (Gaussian, triangular and trapezoidal), the model performed best with the Gaussian function (after 100 iterations, the predicted root mean-square error (RMSE) converged at 0.07%, yielding a smooth linear curve). For a proof-of concept, the model was implemented by using one physician’s gaze behaviors, of which the average, machine-learned fuzzy output probability indicated that the physician was veering toward bottom-up visual attention. This individualized, task-specific pattern of visual attention has implications for the designs of intelligent interface in ICT. Our ANFIS model can scale up to different physicians and tasks to predict the likelihood of bottom-up or top-down information processing based on real-world gaze behaviors.
Implications for Using Mobile Ecological Momentary Assessment to Collect Climate Data from Vulnerable Urban Populations
Authors: Stonewall Jacklin , Dorneich Michael , Passe Ulrike , Shenk Linda
Published: February 2021

This work documents the use of Ecological Momentary Assessment (EMA) to gather data on household behaviors related to outdoor temperatures. In order for urban decision-makers to take effective action to address heat events, they must understand vulnerable residents’ current practices in relation to outdoor temperature. However, vulnerable residents are often excluded from research and can be difficult to reach. EMA allows researchers to gather data in real time and in a subject’s natural environment, which reduces recall bias and improves ecological validity. In this study, EMA was used via a mobile phone app to gather information on home cooling behaviors from residents in vulnerable neighborhoods. Few participants reported making changes to their indoor environments apart from adjusting the thermostat. Despite low participation, the experience of conducting this research has resulted in valuable “lessons learned”. These lessons are used to inform implications and considerations for future use of EMA with vulnerable populations.
Operator Responsibility for Outcomes: A Demonstration of the ResQu Model
Authors: Douer Nir , Redlich Meirav , Meyer Joachim
Published: February 2021

In systems with advanced automation, human responsibility for outcomes becomes equivocal. We developed the Responsibility Quantification (ResQu) model to compute a measure of operator responsibility (Douer & Meyer, 2020) and compared it to observed and subjective levels of responsibility (Douer & Meyer, 2019). We used the model to calculate operators’ objective responsibility in a common fault event in the control room in a dairy factory. We compared the results to the subjective assessments made by different functions in the diary. The capabilities of the automation greatly exceeded those of the human, and the operator should comply with the indications of the automation. Thus, the objective causal human responsibility is 0. Outside observers, such as managers, assigned much higher responsibility to the operator, possibly holding operators responsible for adverse outcomes in situations in which they rightly trusted the automation.
Considering a Meso-Ergonomic Factor: Can Accountability Reduce Errors?
Authors: Salehi Pouria , Chiou Erin K.
Published: February 2021

Accountability is an ill-defined and underexplored concept in job design, particularly in highly proceduralized environments that must operate under both high throughput and high-security expectations. Using x-ray images from the Airport Scanner game, this paper investigates two mechanisms of accountability: an active condition, and a passive condition. Each group was shown a list of prohibited items, but different feedback was provided. The active group was asked to be vigilant for any possible threat, whereas the passive group was instructed to stick to the list. Data from 76 participants were collected. The active group took longer (+16%) to process fewer (-10%) x-ray images compared to the passive group. As a result, the active group accomplished fewer hits (-14%), but also fewer misses (-31%) compared to the other group. While the active group worked slower, they provided higher quality performance. We conclude that accountability mechanisms can reduce errors if applied appropriately.
Predicting Graceful Extensibility of Human-Machine Systems: A New Analysis Method for Evaluating Extensibility Plots to Anticipate Distributed System Performance
Authors: Morey Dane A. , Marquisee Jesse M. , Gifford Ryan C. , Fitzgerald Morgan C. , Rayo Michael F.
Published: February 2021

With all of the research and investment dedicated to artificial intelligence and other automation technologies, there is a paucity of evaluation methods for how these technologies integrate into effective joint human-machine teams. Current evaluation methods, which largely were designed to measure performance of discrete representative tasks, provide little information about how the system will perform when operating outside the bounds of the evaluation. We are exploring a method of generating Extensibility Plots, which predicts the ability of the human-machine system to respond to classes of challenges at intensities both within and outside of what was tested. In this paper we test and explore the method, using performance data collected from a healthcare setting in which a machine and nurse jointly detect signs of patient decompensation. We explore the validity and usefulness of these curves to predict the graceful extensibility of the system.
Human Factors Issues and Challenges In Research on ‘Many- Agent’ Control Applications
Authors: Miller Christopher A. , Adams Julie , Clark Shane S. , Kane Stephanie , Roundtree Karina , Walker Phillip
Published: February 2021

Human control of multiple agents in swarm or multi-agent deployments are still novel and understudied—especially when real-world research involving many agents (e.g., 50+) are considered, though many organizations are rushing to deploy them for diverse purposes from package delivery to military engagements. Challenges include the scale of situation awareness, workload and attention splitting associated with division of labor across many functions, human perceptions of emergent swarm behaviors, etc. The challenges in either fielding many automated agents in real world settings using current technologies, or of accurately simulating those agents and behaviors sufficiently are posing difficulties—and will become ever more difficult as scale and complexity increases. This panel will focus on recent efforts to create and evaluate methods to support human control of large numbers of swarm or multi-agent vehicles, with an emphasis on deployment and lessons from real world “in the wild” evaluations using multiple representative machine agents.
A Kalman estimation model of human trust in supervisory control of robotic swarms
Authors: Li Huao , Lewis Michael , Sycara Katia
Published: February 2021

Trust is an important factor in the interaction between humans and automation that can mediate the reliance of human operators. In this work, we evaluate a computational model of human trust on swarm systems based on Sheridan (2019)’s modified Kalman estimation model using existing experiment data (Nam, Li, Li, Lewis, & Sycara, 2018). Results show that our Kalman Filter model outperforms existing state of the art alternatives including dynamic Bayesian networks and inverse reinforcement learning. This work is novel in that: 1) The Kalman estimator is the first computational model formulating the human trust evolution as a combination of both open-loop trust anticipation and closed-loop trust feedback. 2) The proposed model considers the operator’s cognitive time lag between perceiving and processing the system display. 3) The proposed model provides a personalized model for each individual and reaches a better level of fitness than state-of-the-art alternatives.
Modeling and inferring human trust in automation based on real- time eye tracking data
Authors: Lu Yidu , Sarter Nadine
Published: February 2021

Trust miscalibration remains a major challenge for human-machine interaction. It can lead to misuse or disuse of automated systems. To date, most trust research has relied on subjective ratings and behavioral or physiological data to assess trust. Those trust measurements are discrete, disruptive and quite difficult to implement. To better understand the process of trust calibration, we propose eye tracking as an unobtrusive method for inferring trust levels in real time. Using an Unmanned Aerial Vehicle simulation, participants were exposed to varying levels of reliability of an automated target detection system. Eye movement data were captured and labeled as high or low trust based on subjective trust ratings. Feature extraction and raw eye movement data were compared as input for multiple classification modeling methods. Accuracy rates of 92% and 80%, respectively, were achieved with individual-level and group-level modeling, suggesting that eye tracking is a promising technique for tracing trust levels.
Using Function Allocation Methods to Inform Research Roadmaps: A Human-Robot Interaction Case Study for Medjool Date Thinning
Authors: Salzer Yael , Bass Ellen J.
Published: February 2021

The objective of the work is to develop a roadmap to support robotic Medjool date thinning system (RDTS) human-robot interaction research. The focus of the work relates to the allocation of functions among the human operator and the envisioned RDTS. We reviewed the current status of enabling technologies. We used the abstraction hierarchy multilevel representation framework to identify the physical functions and resources of the envisioned system. By considering the current status and the abstraction hierarchy, we developed four technically possible function allocation options. These analyses formed the basis for discussion to support research roadmap refinement. The researchers are using this refined roadmap to align their goals to ensure a better RDTS research outcome.
The Gap Between The Micro And Macro Scales of Teamwork is Spanned By Neural Information But Not Neural Band Power
Authors: Stevens Ron , Galloway Trysha
Published: February 2021

One of the more intriguing questions about teamwork is how macro-scale behaviors arise from the micro-scale neural dynamics of each team member. In this paper we show that the low-level dynamics of EEG band power, which themselves link poorly with behavioral teamwork measures can be transformed into information-based units of neurodynamic organization that link with macro-level constructs like experience and uncertainty. We suggest it is the frequency, magnitude and durations of the temporal organizations within the EEG band power that are important for linking to macro behaviors, and not exclusively EEG power levels. These studies provide an avenue for understanding how complex organizations arise from the dynamics of underlying micro-scale variables. The study also has practical implications for how micro-scale variables might be better represented, both conceptually and in terms of parsimony, for training machines to recognize human behaviors that span scales of teamwork.
Come Closer: Experimental Investigation of Robots’ Appearance on Proximity, Affect and Trust in a Domestic Environment
Authors: Miller Linda , Kraus Johannes , Babel Franziska , Messner Matthias , Baumann Martin
Published: February 2021

From once being utopian, robots become more and more common in public and private social life. One area of application are robots, which are able to perform chores in domestic environments. To support users accustoming to the new robot companion, psychological mechanisms that enhance the interaction with robots need to be understood. In this research, the effects of robot’s height and manipulator position on proximity, affect and trust with regards to a humanoid robot were investigated in a laboratory experiment. Results indicated an effect of robot’s height on the comfort distance. On the other hand, a habituation effect after a second robot-human approach was found, in which the influence of the robot’s height on proximity diminished. Similar effects with an anxiety decrease and trending trust increase in the tall robot over time emphasize the importance of first encounters, which should be paid special attention to by designers of robotic systems.
Challenges and Opportunities in Collaborative Vulnerability Research Workflows
Authors: Mullins Ryan , Kelliher Deirdre , Nargi Ben , Keeney Mike , Schurr Nathan
Published: February 2021

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.
Modeling Information Pooling Bias in Incident Response Teams: An Agent Based Modeling Approach
Authors: Rajivan Prashanth , Cooke Nancy J.
Published: February 2021

Security analysts regularly correlate disparate incidents to detect cyber-attacks. However, past research shows that team-based incident correlation analysis may be affected by information pooling bias. This article presents findings from an agent-based model used to explore the cognitive processes hypothesized to be causing this bias during information exchange within a team. The model simulated information exchange between three analysts conducting incident correlation analysis by searching for information available with them about the different incidents. Three models of memory search process were compared: Random, Local, and Memory-aided search. Results from the simulation show that agents in a local search model, compared to memory-aided search model, shared more often the information known to majority in the team. Comparing model results with data from lab experiments suggest that teams, by default, may be employing a heuristic search process during information exchange leading to sub-optimal team processes and performance.
What Attackers Know and What They Have to Lose: Framing Effects on Cyber-attacker Decision Making
Authors: Cranford Edward A. , Gonzalez Cleotilde , Aggarwal Palvi , Tambe Milind , Lebiere Christian
Published: February 2021

Many cybersecurity algorithms assume adversaries make perfectly rational decisions. However, human decisions are only boundedly rational and, according to Instance-Based Learning Theory, are based on the similarity of the present contextual features to past experiences. More must be understood about what available features are represented in the decision and how outcomes are evaluated. To these ends, we examined human behavior in a cybersecurity game designed to simulate an insider attack scenario. In a human-subjects experiment, we manipulated the information made available to participants (concealed or revealed decision probabilities) and the framing of the outcome (as losses or not). An endowment was given to frame negative outcomes as losses, but these were not framed as losses when no endowment was given. The results reveal differences in behavior when some information is concealed, but the framing of outcomes only affects behavior when all information is available. A cognitive model was developed to help understand the cognitive representation of these features and the implications of the behavioral results.
Demonstration: Virtual Patient Immersive Trainer to Train Perceptual Skills Using Augmented Reality
Authors: Sushereba Christen E. , Militello Laura G.
Published: February 2021

In this session, we will demonstrate the Virtual Patient Immersive Trainer (VPIT). The VPIT system uses augmented reality (AR) to allow medics and medical students to experience a photorealistic, life-sized virtual patient. The VPIT supports learners in obtaining the perceptual skills required to recognize and interpret subtle perceptual cues critical to assessing a patient’s condition. We will conduct an interactive demonstration of the virtual patient using both a tablet (for group interaction) and an AR-enabled headset (Microsoft HoloLens) for individual interaction. In addition, we will demonstrate use of the instructor tablet to control what the learner sees (e.g., injury types, severity of injury) and to monitor student performance.
The Education of the Human Factors Engineer in the Age of Data Science
Authors: Hannon Daniel , Rantanen Esa , Sawyer Ben , Hughes Ashley , Darveau Katherine , O’Donnell Robert , Intriligator James , Lee John D.
Published: February 2021

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.
Educational Data Mining and Learning Analytics for Improving Online Learning Environments
Authors: Paredes Yancy Vance , Siegle Robert F. , Hsiao I-Han , Craig Scotty D.
Published: February 2021

The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However, these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems, it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA, discuss data visualization and dashboards that are used to convey results to learners, and finally to identify where human factors can aid other fields.
Human-Centered and Psychological Concepts in Undergraduate Engineering Project Documentation
Authors: Roscoe Rod D. , Arnold Samuel T. , Johnson Chelsea K.
Published: February 2021

The success of engineering and design is facilitated by a working understanding of human thoughts, feelings, and behaviors. In this study, we explored how undergraduate engineering students included such human-centered and psychological concepts in their project documentation. Although, we observed a range of concepts related to design processes, teams, cognition, and motivation, these concepts appeared infrequently and superficially. We discuss how this analysis and approach may help to identify topics that could be leveraged for future human-centered engineering instruction.
A Case Study Demonstrating the Utility of Surveillance Video in Assessing Gait Dynamics and Behavior During a Slip and Fall
Authors: Hartman Leah S. , Borzendowski Stephanie A. Whetsel , Campbell Alan O.
Published: February 2021

As the use of surveillance video at commercial properties becomes more prevalent, it is more likely an incident involving a personal injury will be captured on film. This provides a unique opportunity for Human Factors practitioners involved in forensic investigations to analyze the behavior of the individual prior to, during, and after the event in question. It also provides an opportunity to gather unique and objective data. The present work describes a case study of a slip and fall where surveillance video and onsite measurements were combined and analyzed to quantify a plaintiff’s gait pattern. Using this type of analysis, we were able to determine that the plaintiff was likely aware that the floor was slippery and adjusted her gait and behavior prior to the slip and fall incident.
HFES Technical Standards Update
Authors: (Bob) Fox Robert R. , (Ram) Maikala Rammohan , Albin Tom , Rempel David , (Bob) Fox Robert R. , See Judi E.
Published: February 2021

HFES oversees or touches on a variety of ergonomics and human factors standards activities including standards developed within HFES (e.g. HFES 100) and standards work outside of HFES such as ANSI, ISO and ACGIH. This session is intended to update HFES members on the standards work that HFES is involved. This year the emphasis will be on specific new standards work on topical areas. Included will be a discussion of a suggested interpretation strategy for the outcome metrics of the Revised NIOSH Lift Equation (RNLE) that came out of work on the revision of the ISO 11228 Part 1 on Lifting, Lowering and carrying, the status of the HFES 100 Human Factors Engineering of Computer Workstations standard and the latest work on the ACGIH Ergonomics standards and on a proposed ANSI standard on Human Readiness Levels.
Comparing Physician and Nurse Opinions of Team and Individual Handoffs: Does Role Influence Preference?
Authors: Heuck Emily , Wooldridge Abigail
Published: February 2021

Care transitions are key to patient safety and remain a safety issue despite previous research. This study examines how the design of care transitions impacts different health care professions. Twenty-nine physicians and nurses were interviewed about operating room to intensive care unit care transitions. We compared relationships between work system elements in positive and negative opinions about two sociotechnical system designs: including team or individual handoffs. Nurses did not express positive opinions of individual handoffs or negative opinions of team handoffs, while physicians expressed positive and negative opinions of both. Relationships between work system elements varied by profession in the positive opinions about team handoffs and negative opinions about individual handoffs. Professional needs and culture may be related to the different perceptions of each handoff. Future work should continue to examine professional differences when developing a flexibly standardized process to ensure all users are considered.
The Patient Factor: Involving Patient and Family Stakeholders as Advisors, Co-Designers, Citizen Scientists, and Peers
Authors: Holden Richard J. , Valdez Rupa S. , Anders Shilo , Ewart *Colleen , Lang *Alexandra , Montague Enid , Valdez Rupa , Zachary Wayne
Published: February 2021

In health and healthcare, patients, families, and other stakeholders increasingly participate in designing, executing, and evaluating initiatives likely to affect them. Stakeholder participant roles include: (1) Advisors, as in patient/family advisory councils; (2) Co-Designers, as in participatory design team members; (3) Citizen Scientists, as in community-based participatory research; and (4) Peers, as in peer reviewers of proposed research or decisions regarding the ethics of a planned research study. This panel assembles leading US and UK human factors experts to discuss how stakeholders can be and have been involved as participants in the design, implementation, or evaluation of health or healthcare related initiatives. Panelists describe: (a) how they have involved stakeholders, describing their methods, strategies, and results; (b) associated benefits they have observed; (c) needs that have surfaced and must be addressed for successful stakeholder participation; and (d) recommendations for human factors professionals planning to involve stakeholder participants.
Use of Wearable Sensors and Machine Learning Methods in Promoting Total Joint Replacement Treatment Outcomes: A Survey
Authors: Yerebakan Mustafa Ozkan , Zhong Xiang , Parvataneni Hari K. , Gray Chancellor F. , Hu Boyi
Published: February 2021

Total Joint Replacement (TJR) surgeries are one of the most prevalent operations that are undergone by the elderly population. With the world population aging, the number of surgeries will continue to increase. A small portion of these surgeries result in complications that require readmissions. These readmissions amount to a significant financial and time burden for both the patients and the hospitals. In the past decade machine learning and wearable sensors have both been used extensively in the healthcare domain but the contribution to the prediction/evaluation and management of TJR is limited. What’s more, to our best knowledge there has been no effort in summarizing the findings from these studies. Therefore, this study highlights what has been achieved by using machine learning and wearable sensors in the TJR context and point out possible research avenues.
Surgeon Postures during Deep Inferior Epigastric Perforator Flap Breast Reconstruction Procedures: A Pilot Study
Authors: Wang Tianke , Law Katherine E. , Harless Christin , Nguyen Minh-Doan , Susan Hallbeck M.
Published: February 2021

Deep inferior epigastric perforator (DIEP) flap is a surgical procedure using tissue harvested from the abdominal area to reconstruct the breast after a mastectomy. Primary surgical phases include the abdominal flap harvest, chest dissection, and anastomosis. In this study, inertial measurement units (IMUs) were used to measure surgeon upper body postures while performing DIEP flap procedures. Ergonomic risks of musculoskeletal disorders were evaluated using the Rapid Upper Limb Assessment (RULA) based on the postures obtained from the IMUs. Joint angles were analyzed using a one-way ANOVA. The neck and back had higher joint angles during the abdominal flap (M=32°, 17°, respectively) and chest dissection (M=31°, 18°, respectively) phases than the anastomosis phase. These high-risk postures may lead to musculoskeletal disorders. Future interventions should focus on improving the postures of the neck and back during the abdominal flap and chest dissection phases of the procedure.
Task Analysis on Yoga Poses Toward a Wearable Sensor-based Learning System for Users with Visual Impairment
Authors: Grudinschi Melanie , Norland Kyle , Won Lee Sang , Lim Sol
Published: February 2021

People with visual impairments may experience difficulties in learning new physical exercises due to a lack of visual feedback. Learning and practicing yoga is especially challenging for this population as yoga requires imitation-oriented learning. A typical yoga class requires students to observe and copy poses and movements as the instructor presents them, while maintaining postural balance during the practice. Without additional, nonvisual feedback, it can be difficult for students with visual impairments to understand whether they have accurately copied a pose – and if they have not, how to fix an inaccurate pose. Therefore, there is a need for an intelligent learning system that can capture a person’s physical posture and provide additional, nonvisual feedback to guide them into a correct pose. This study is a preliminary step toward the development of a wearable inertial sensor-based virtual learning system for people who are blind or have low vision. Using hierarchical task analysis, we developed a step-by-step conceptual model of yoga poses, which can be used in constructing an effective nonvisual feedback system. We also ranked sensor locations according to their importance by analyzing postural deviations in each pose compared to the reference starting pose.
Predicting The Future: Diverse Perspectives on The Transformation of Healthcare Delivery over the Next 15 Years
Authors: Patterson Emily S. , Anders Shilo , Militello Laura G. , Saleem Jason J. , Perry Shawna , Rothwell Clayton , Weiler Dustin
Published: February 2021

Over the last 15 years, healthcare delivery has experienced enormous changes across every aspect of clinical care, both for participants in it and affiliated stakeholders. The plethora and breadth of these changes and ongoing struggles with their incorporation into the work of healthcare begs the question: In 2035, what emerging innovations and considerations will we wish we had prepared for with regard to human factors engineering research, technology development, and system redesign efforts? Panelists and co-chairs have deep experience with steering, and being impacted by, innovations in information technology in healthcare. The panelists represent diverse academic, clinical, government contract research and development, and small entrepreneurial business. Perhaps the biggest benefit of this session will be the explicit inclusion of the audience in brainstorming and discussion activities, which will be facilitated digitally and verbally during the session and shared afterwards electronically with participants.
Identifying roles in older adults’ emergency department transitions
Authors: Salwei Megan E. , Barton Hanna , Werner Nicole E. , Rutkowski Rachel , Hoonakker Peter L.T. , Wust Kathryn , Shah Manish N. , Patterson Brian W. , Pulia Michael S. , Hamedani Azita G. , Smith Maureen , King Barbara , Dail Paula vW. , Carayon Pascale
Published: February 2021

Older adults frequently visit the emergency department (ED) and participate in multiple transitions of care following an ED visit. These transitions of care, e.g. to hospital, long-term care facility or home, represent patient safety risks because of communication and coordination failures between the various roles involved, but also provide opportunities for error detection and recovery and, therefore, resilience. The objective of this study was to identify and describe the multiple roles involved in older adult care transitions during an ED visit. As part of a large research project, we conducted patient-centered observations and interviewed ED clinicians and hospital administrators. We identified 16 ED roles involved in older adult care transitions out of the ED, including 4 roles solely focused on coordinating transitions. By better understanding the roles involved in ED care transitions, we can improve the design of team processes and technologies to support care of older adults throughout their care transitions.
Correlating Physiologic Measures of Stress: Exploring Dyads in Clinical Surgical Teams
Authors: Fong Allan , Fitzgibbons Shimae , Sava Jack , Wang Weiguang , Wegener Nicholas R. , Christian James G. , Hall Erin C.
Published: February 2021

Clinical teams are subject to stress from various sources, including the technical and cognitive challenges of providing care in high stakes environments. Existing analytic approaches are limited in their ability to study the interdependence of team member stress. This study explores the correlation of a physiologic marker of stress, blood pulse wave, between members of a working surgical team. We propose an area overlap method as a means of evaluating blood pulse wave time-series correlation as a function of time. This is a stepwise approach to the collection and analysis of a large volume of continuous physiologic data from paired team members in a clinical setting. This method was applied to thirteen surgical team dyads with similar results to Pearson correlation. The area overlap method allows for improved exploration of temporal correlation within dyads but, in its current form, does not identify directionality of correlation.
Ergonomic Process Improvement for Mapping Risk and Identifying Key Interventions for Eliminating Injuries from Needles and Sharps in the OR
Authors: Kalaga Pramila , Wolford Barbara , Mormino Matthew , Kingston Timothy , Fedderson Julie , Husk Heidi , Smith Suzanne , O’Brien Missy , Thompson Courtney , Vaughn Kelly , Miller Scott , Berg Kelly , Delaughter Emily , Kellar Jacquelyn , Trimble Stefanie , Lowndes Bethany
Published: February 2021

The risk of a needle stick or sharps injury in the operating room (OR) is high due to conditions such as minimal physical protective measures, frequent transfer of sharps, and reliance on human attention and skill for injury avoidance. An ergonomic process improvement project was initiated at a large metro teaching hospital to identify ergonomic risk factors for these OR injuries. To maximize the engagement of the front- end users, an ergonomic process improvement (EPI) team was developed, consisting of representatives from participating OR teams, an employee health nurse and two ergonomists. Surveys, observations, and interviews were conducted to quantify injury risk for the OR teams, evaluate barriers to best practice adherence, and identify opportunities for targeted interventions. Risk mapping was completed for the surgeons, surgical techs and OR nurses identifying double gloving and safe passing zone as areas in need of improvement. Through observation and interviews, researchers identified physical factors relating to musculoskeletal pain and cognitive factors leading to distractions as safety risk concerns. The overall success of the EPI was the engagement of the OR teams and surgeons in the process of identifying risk factors and potential opportunities for ergonomic solutions related to cognitive workload, physical workload, teamwork, and work design for injury prevention. The risk factors identified will provide the basis for developing targeted, effective interventions for eliminating injuries from needles and sharps within the OR.
Correlation Between Gestures’ Qualitative Properties and Usa- bility metrics
Authors: Chanci Daniela , Madapana Naveen , Gonzalez Glebys , Wachs Juan
Published: February 2021

The choice of best gestures and commands for touchless interfaces is a critical step that determines the user- satisfaction and overall efficiency of surgeon computer interaction. In this regard, usability metrics such as task completion time, error rate, and memorability have a long-standing as potential entities in determining the best gesture vocabulary. In addition, some previous works concerned with this problem have utilized qualitative measures to identify the best gesture. In this work, we hypothesize that there is a correlation between the qualitative properties of gestures (v) and their usability metrics (u). Therefore, we conducted an experiment with linguists to quantify the properties of the gestures. Next, a user study was conducted with surgeons, and the usability metrics were measured. Lastly, linear and non-linear regression techniques were used to find the correlations between u and v. Results show that usability metrics are correlated with the gestures’ qualitative properties ( R2 = 0.4).
User-centered Requirements for Patient Fluid Intake Monitoring Support
Authors: Bass Ellen J. , Abbate Andrew J. , Noaiseh Yaman , Ann DiMaria-Ghalili Rose
Published: February 2021

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis.
Users’ Needs in Designing a Mobile Dialysis Device. Insights from an Interview in a Mixed Methods Study
Authors: Jónsdóttir1 Auður Anna , Kessler Larry , Rim Seung Yeon , Kim Ji-Eun
Published: February 2021

The goal of this study is to identify ways to increase the usability of a mobile dialysis device through semi-structured interviews during the early stage of product development. We analyzed interview data using a mixed method of rank order and open-ended questions. The results from the open-ended questions showed that patients and care partners prefer the device being carried on patients’ backs, followed by the waist and the shoulder. We also identified 13 key categories of ideal features that the mobile dialysis device must hold. The results from the rank order question showed that the participants significantly preferred the Vest over the Backpack, Shoulder bag, and Distributed model. Accuracy of the device is more important compared to Safety, Ease of attachment, Comfort, Size, Operational simplicity, and Invisibility. The prioritized needs from patients and care partners provide designers and clinicians with better target solutions for designing a mobile dialysis device.
User and Task Analysis for Evaluation of Clinical Decision Support for Quality Improvement
Authors: Kandaswamy Swaminathan , Karavite Dean , Muthu Naveen , Shaeffer Gerald , Grundmeier Robert , Tobias Marc , Zeidlhack Mike , Orenstein Evan
Published: February 2021

Clinical decision support (CDS) is a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information that can significantly improve health outcomes and healthcare delivery. However, their impact on clinical outcomes has been inconsistent. Rigorous and continuous evaluation of CDS is necessary for improving CDS. A User and Task analysis was conducted to understand the stakeholder roles, their goals and tasks involved in the evaluation of CDS. This study describes a framework for evaluating CDS effectiveness for improving quality outcomes based on the analysis.
The Prediction of Collisions in Connected Vehicle Systems with A Long Short-Term Memory Model
Authors: Mishra Rohit , Zhang Yiqi , Ma Fenglong , Li Anlong
Published: February 2021

The advances in connected vehicle systems (CVS) allow vehicles to communicate with each other and with infrastructures via wireless communication networks. This technology enables vehicles to detect potential hazards on the road, generate warnings, and assist the driver in taking preventive actions. To date, few mathematical models have been developed to predict the collision rates in connected vehicle systems. In this work, a Long Short-Term Memory model (LSTM) using time-series data of human drivers was developed to predict the collision rates in CVS by quantifying warning parameters and hazard scenario features. The model was validated with the driving performance data before and after warnings from thirty-two drivers in a behavioral experiment. The results indicated the LSTM model showed a prediction accuracy of 74% higher than SVM and logistic regression models. The LSTM model showed the potential to help optimize the warning algorithm in the connected vehicle systems to improve driver safety.
Cognitive Work Analysis and Visualization Design for the Graduate Admission Decision Making Process
Authors: Wang Xiaomei , Bisantz Ann M. , Bolton Matthew L. , Cavuoto Lora , Chandola Varun
Published: February 2021

Graduate admission has always been a complex decision making process. The link between application materials and student success has remained elusive and, as such, there is no validated method for making decisions. To understand the purposes, processes, difficulties and needs of the current graduate admission process, semi-structured interviews were conducted with participants from engineering departments. Cognitive work analysis techniques were used to summarize the findings from the interviews. Visualizations were designed to improve the current online review system. User feedback was collected in an experiment.
Inclusion by Design: A 75-Minute Crash Course on Accessible Design
Authors: Brinkley Julian , Huff Earl W.
Published: February 2021

The community of researchers supporting instruction on design thinking has a significant body of materials to help students understand and master the process of creative problem solving in design. Missing, we argue are materials and processes which directly support the design of inclusive technologies for persons with disabilities. We present ‘Inclusion by Design’, an interactive and participative crash course designed to introduce students to techniques that may be useful in an inclusive design process. In a single 75-minute session, students explore the inclusive design of a transportation technology for a visually impaired persona. We report on our findings from a single pilot of the crash course involving six diverse students within a graduate course on Inclusive Design. Our findings suggest that the course may be effective in introducing techniques like storyboarding, scenario creation, and low fidelity prototyping to students using an approach that may be effective for various learning styles.
“What the Organization Has”: An Investigation of Situational Aspects of Safety Culture of Road Traffic Organization Using a Macroergonomic Approach
Authors: Andrijanto  , Makoto Itoh , Gabariel Pangaribuan Alphared
Published: February 2021

This research applied a reciprocal model to investigate the safety culture of road traffic organizations for motorcyclists’ safety. By focusing on the situational aspects, we applied the four steps of the macroergonomic approach to analyze a local government organization in Indonesia. We identified some weak elements of safety culture embedded in the sub-system level of the organization. The absence of proper education of motorcyclist candidates has led to failures in developing their skills and knowledge. In addition, we found that the existence of the item “if any” in the licensing registration procedure weakened the understanding of the importance of learning. Investigation of situational aspects revealed some critical issues regarding safety culture development by road traffic organizations.
Exploring Informal Caregiver Workload using a Macroergonomics Lens on Multiple Resources
Authors: Ponnala Siddarth , Werner Nicole E.
Published: February 2021

Persons with dementia (PwD) are an increasing population that are becoming more dependent on informal caregivers. Thus, caregiver workload is becoming a national concern due to the amount of patient work associated with caregiving. While we have a rudimentary understanding of caregiving work demands, caregiving capacity is an area that needs further inquiry. In this study, we apply a macroergonomic approach to identify non-cognitive resources employed by caregivers, and contingencies for caregivers’ resource usage. We conducted 30 semi-structured interviews with self-identified primary caregivers of persons with dementia. Using the patient work system model, we conducted content and thematic analyses of interview data to build a conceptual model of caregiving capacity. Our conceptual model includes 6 different resources channels that caregivers may activate. We also identified 3 contingencies of resource usage. This model can serve as a foundation for exploring distribution of caregiver workload among non- cognitive resources.
Assessing Psychosocial and Personal Factors in Industrial Work: Issues and Challenges for the Occupational Ergonomics Practitioner and Researcher
Authors: (Bob) Fox Robert R. , Smith Missie , Bao Stephen , Barim Menekse , Gallagher Sean , Harris-Adamson Carisa , (Jack) Lu Ming-Lun , Nigam Jeannie
Published: February 2021

The occupational ergonomics practitioner has traditionally focused on physical aspects and risk factors of industrial jobs (e.g., forces exerted, moments and loading on the joints, low back compressive force, upper extremity repetitive motion, etc.). However, some studies have shown that physical risk factors may only account for about 37% of low back pain (Punnett et al., 2005). Psychosocial factors continue to be identified as an important element in the incidence of many occupationally-related musculoskeletal disorders. Some researchers have included assessments of psychosocial and personal/individual factors to biomechanical assessments in studies of occupational injury risks (e.g., Marras et al., 2000, Ferguson et al., 2012). Others (e.g., Barim et al., 2019) have examined incorporating personal/individual factors into risk assessment methods to give a more complete picture of injury risk that an individual or a subgroup of workers (e.g., gender, age, prior injury) may face on jobs.

This discussion panel will explore the research in the fields of psychosocial and personal risk factors and their relevance to the assessment of injury risk. This session is relevant for the practitioners who must understand, interpret and apply the results of research to real-world problems.