bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Procedural Elements on Trust and Compliance with anImperfect Decision Aid
Authors: Pharmer Rebecca L. , Wickens Christopher D. , Clegg Benjamin A. , Smith C.A.P
Published: November 2021

We sought to establish to what extent incorporating a dichotomized procedural variable (in this case, maritime ‘rules of the road’) and incentives into a decision aiding algorithm would change a previously found non-compliance bias when the algorithm contradicted the known procedure. We also sought to examine the relationship between trust in and dependence on an automated system. An experiment was conducted using a simple, simulated maritime collision avoidance task featuring an imperfect, but highly reliable (87%), decision aid. Adding the dichotomous procedural variable into the algorithms recommendations increased compliance with the system, even for recommendations that violated learned procedures. Performance was still not perfectly calibrated to the actual reliability of the system (underreliance and under-trust). Results also revealed the dissociation between rated trust in, and behavioral dependence on decision aiding automation.
Cub Companion: Aiding The Establishment of Trust Between Patients and Nurses In Pediatric Oncology Units Via Technology And Augmented Reality
Authors: Hasbrouck C. R. , Starkey Elizabeth M.
Published: November 2021

The purpose of this study is to investigate the needs of pediatric cancer patients to alleviate some of the stress associated with their treatments and hospitalization. Two pediatric nurses and six parents of children with childhood cancer diagnoses were interviewed to determine the best methods of stress reduction during cancer treatments. Using the information gathered, a qualitative content analysis was conducted using pre-coded themes to determine prevalent ideas throughout the interviews. Themes that emerged for reducing stress included building trust with parents and medical staff, becoming comfortable in a new environment, and distraction during invasive procedures. An augmented reality concept was tested using a medium-fidelity prototype consisting of a customizable teddy bear and Adobe XD mobile application simulation. The evidence collected during interviews and medium-fidelity prototype testing shows great promise for Cub Companion’s ability to provide distraction during treatments and help the child build trust with medical personnel.
A Comparison of Auditory and Visual Representations of System Confidence to Support Trust Specificity, Attention Management, and Joint Performance in Human-Machine Teams
Authors: Lieberman Kevin , Sarter Nadine
Published: November 2021

Breakdowns in human-robot teaming can result from trust miscalibration, i.e., a poor mapping of trust to a system’s capabilities, resulting in misuse or disuse of the technology. Trust miscalibration also negatively affects operators’ top-down attention allocation and monitoring of the system. This experiment assessed the efficacy of visual and auditory representations of a system’s confidence in its own abilities for supporting trust specificity, attention management and joint performance in the context of a UAV-supported target detection task. In contrast to earlier studies, neither visual nor auditory confidence information improved detection accuracy. Visual representations of confidence led to slower response times than auditory representations, likely due to resource competition with the visual target detection task. Finally, slower response times were observed when a UAV incorrectly detected a target. Results from this study can inform the design of visual and auditory representations of system confidence in human-machine teams with high attention demands.
Mitigating the Effects of Task Switching in Baggage Screeners
Authors: Zish Kevin , Eisert Jesse , Blanchard Jennifer , Endres Daniel , Band David , Korbelak Kristopher , McKnight Simone , McKee Charles
Published: November 2021

Using a simulated baggage screening task, we investigated two literature-supported mitigation strategies for reducing the negative effects of task switching, namely less frequent switching and memory support. The study replicates widely reported switching effects on a complex task. The results also show that people can improve performance when provided memory support. When task switching, people can struggle to retrieve the correct task instruction due to the automatic process behind functional memory decay. Memory support reduces the negative effects of functional decay by providing people a reminder.
Using a Binary Classification Approach to Assess the Accuracy of Hand Posture and Force Estimation with Machine Learning Models
Authors: Wang Mengcheng , Zhao Chuan , Barr Alan , Yu Suihuai , Kapellusch Jay , Harris Adamson Carisa
Published: November 2021

Recent studies have successfully reported the accuracy of using artificial neural networks to predict grip force in controlled settings. However, only relying on accuracy to evaluate the machine learning models may lead to overoptimistic results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC) showed an advantage in capturing all the data characteristics in the confusion matrix. Therefore, a binary classification approach and the MCC value were introduced to assess the performance of previously proposed machine learning models. Our results show that the overall correlations ranging between 0.48 and 0.59 indicate a strong relationship between predictions and actual scenarios. The binary classification approach and the MCC values could be used for future performance comparison with other machine learning models.
Investigating the Impact of Visual Environmental Uncertainty on Human-Agent Teaming
Authors: Daronnat Sylvain , Azzopardi Leif , Halvey Martin
Published: November 2021

Uncertainty in Human-Agent interactions is often studied in terms of transparency and understandability of agent actions. Less work, however, has focused on how Visual Environmental Uncertainty (VEU) that restricts or occludes visual information affects the Human-Agent Teaming (HAT) in terms of trust, reliance, performance, cognitive load and situational awareness. We conducted a mixed-design experiment (n=96) where participants interacted with an agent during a collaborative aiming task under four different types of VEUs involving global and dynamic occlusions. Our results show that while environmental uncertainties led to increases in perceived trust, they induced differences in reliance and performance. Counter to intuition, when participants trusted the agent the most, they relied on the agent more, but performed worst. These findings highlight how trust in agents is also influenced by external environmental conditions and suggest that reported trust in HAT scenarios may not always generalize beyond the environmental factors in which they were studied.
Role of Peripheral Vision in Brake Reaction Time During Safety Critical Events
Authors: Sarkar Abhijit , Alambeigi Hananeh , McDonald Anthony , Markkula Gustav , Hickman Jeff
Published: November 2021

The criticality of a rear end event depends on the brake reaction time (BRT) of the driver. Therefore, distracted driving poses greater threat in such events. Evidence accumulation model (EAM) that uses looming of the lead vehicle as main stimuli has shown significant success in estimating drivers’ BR Ts. It is often argued that drivers collect evidence for braking through peripheral vision, especially during off-road glances, and transition to forward. In this work, we have modeled evidence accumulation as a function of gaze eccentricity for off-road glances while approaching safety critical events. The model is tested with real world crash and near crash event data from SHRP2 naturalistic study. Our model shows that linear relation between gaze eccentricity and evidence accumulation rate during off road glances helps to improve EAM estimation in predicting BRT. We have also shown that brake-light onset does not influence EAM in presence of active looming.
A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems
Authors: Luster Maya S. , Pitts Brandon J.
Published: November 2021

The field of Cyber-Physical Systems (CPS) is increasingly recognizing the importance of integrating Human Factors for Human-in-the-loop CPS (HiLCPS) developments. This is because psychological, physiological, and behavioral characteristics of humans can be used to predict human-machine interactions. The goal of this pilot study is to collect initial data to determine whether driving and eye tracking metrics can provide evidence of learning for a CPS project. Six participants performed a series of 12 repeated obstacle avoidance tasks in manual driving. Lane deviations and fixation-related eye data were recorded for each trial. Overall, participants displayed either conservation/safe or aggressive/risky in their lateral position with respect to the obstacle during successive trials. Also, eye tracking metrics were not significantly affected by trial number, but observational trends suggest their potential for aiding in understanding adjustments humans make in learning. Results can inform predictive modeling algorithms that can anticipate and mitigate potential problems in real-time.
Racing in the Dark: Exploring Accessible Virtual Reality by Developing a Racing Game for People who are Blind
Authors: Gluck Aaron , Boateng Kwajo , Brinkley Julian
Published: November 2021

Video games are popular leisure and social activities; however, they often present accessibility issues for the community of gamers who are blind. While gaming technology has evolved, people who are blind still have limited access to commercially available accessible video games. Current accessible games tend to focus on auditory information to play, and none are available in virtual reality (VR). Accessible, fast-paced games have the development challenge of providing players useful information in time to make split-second decisions. We developed Racing in the Dark, a fast-paced, accessible VR racing game for the Oculus Quest, exploring commercial VR interfaces to provide this critical information. By leveraging the Quest’s built-in haptic, tracking, auditory, and voice systems, we provide players with a non-visual car racing experience. This work aims to offer insight into the process of developing accessible VR games for gamers who are blind through the implementation of Racing in the Dark.
The Daily Experiences of Facebook for Visually Impaired Users
Authors: Huﬀ Earl W. , Gluck Aaron , Boateng Kwajo , Brinkley Julian
Published: November 2021

Prior research into using social networking sites (SNSs) by visually impaired (VI) users has focused primarily on their competencies and accessibility barriers in its usage. We argue, however, that there exists insuﬃcient research into the daily pattern of use of SNSs by VI users and beneﬁts unique to this population. To address these limitations, we conducted two empirical studies, a diary study and a follow-up in-depth interview study, to further explore the social experiences of VI users’ everyday Facebook engagement. Our ﬁndings show that VI users were motivated to use Facebook by the prospect of connecting with family and friends and that Facebook oﬀers unique beneﬁts to VI users in terms of improved quality-of-life through social capital building, knowledge obtainment, and entertainment.
The Influence of Feedback Types on the Use of Automation During Learning
Authors: Brunsen Emily , Murph Imani , McLaughlin Anne C. , Wagner Richard B.
Published: November 2021

This study investigated two feedback types to see if there was a relationship between level of elaborative feedback and participant’s ability to learn a task while also looking at their use of automation. The task was a rule-based problem solving task where participants needed to learn the rules of selecting one pair (Ranger and Hiker) on a gridded map. Ten trials were randomly presented to participants who were asked to make pair selections based on rules (two stated and one unknown to the participant when starting) of correct matches. Results indicated that feedback type significantly influenced accuracy, while trial difficulty influenced use of automation. Results from this study can be applied in education and training of declarative knowledge tasks when rules must be inferred.
The Experience Of Public Space And Its Unfolding In Times Of Social Distance
Authors: Gouvea Laura , Mont’Alvao Claudia
Published: November 2021

This paper presents the current scenario in which the relationship between Cariocas and free public leisure spaces of the city of Rio de Janeiro is found: a supposed reduction in the population’s interest in public spaces, encouraged by the technological advances of the last decades, suffers a fulminating blow with the emergence of the new coronavirus and the adoption of social isolation as a protective measure. Social (and political) activities become exclusively virtual, while the streets turn out empty. The individualizing process that was already sneakily manifesting itself is now imposing itself. Does the impossibility of being physically in the city’s spaces cause an increase in the desire to experience these places? Trying to understand the momentary situation better, we applied a questionnaire as a method of collecting information. This questionnaire was designed, tested and applied, and its results are presented in detail to point out possible intercessions between society, the city and the practice of social distancing. The objective of the present work, to understand the relationship between Cariocas and the city’s PLSLs, led us not only to confirm that this relationship exists, but also to the understanding that it is so necessary for the quality of life of society that it ‘survives’ in the face of the numerous problems pointed out.
Research-Based Forensic Analysis of a Vehicle Crash When Limited Information is Available: A Case Study
Authors: Borzendowski Stephanie A. Whetsel , Hartman Leah S.
Published: November 2021

Forensic human factors analyses of personal injuries, including collisions, can be challenging when there is either limited evidence available and/or conflicting accounts of the events leading up to an incident. Gathering objective data under conditions representative of the incident in question can assist human factors practitioners in assessing the plausibility of certain facts and events, particularly in the aforementioned scenarios. The present work is part of the preliminary investigation of a collision in which little information was available about the incident. Data were gathered from nine participants to assist in determining a reasonable timeline of events leading up to impact between a disabled vehicle and a tractor trailer. The importance of reliance on such objective data as part of forensic analyses is discussed.
Advanced Driver Assistance Systems (ADAS): Who’s Driving What and What’s Driving Use?
Authors: Palac Daniel , Scully Iiona D. , Jonas Rachel K. , Campbell John L. , Young Douglas , Cades David M.
Published: November 2021

The emergence of vehicle technologies that promote driver safety and convenience calls for investigation of the prevalence of driver assistance systems as well as of their use rates. A consumer driven understanding as to why certain vehicle technology is used remains largely unexplored. We examined drivers’ experience using 13 different advanced driver assistance systems (ADAS) and several reasons that may explain rates of use through a nationally-distributed survey. Our analysis focused on drivers’ levels of understanding and trust with their vehicle’s ADAS as well as drivers’ perceived ease, or difficulty, in using the systems. Respondents’ age and experience with Level 0 or Level 1 technologies revealed additional group differences, suggesting older drivers (55+), and those with only Level 0 systems as using ADAS more often. These data are interpreted using the Driver Behavior Questionnaire framework and offer a snapshot of the pervasiveness of certain driver safety systems.
Differences in the association between the 2018 ACGIH TLV for Hand Activity and carpal tunnel syndrome by gender and age
Authors: Harris-Adamson C , Meyers A , Bonfiglioli R , Kapellusch J , Dale AM , Thiese M , Fethke N , Eisen EA , Bao S , Evanoff B , Hegmann K , Gerr F , Violante FS , Rempel D
Published: November 2021

The recently revised ACGIH TLV for Hand Activity (TLV2018) is a widely used tool for assessing risk for upper limb musculoskeletal disorders. The purpose of this analysis was to compare the strength of the exposure-response relationships between the TLV2018 and carpal tunnel syndrome (CTS) between men and women and across age strata. Heterogeneity of the effect size by sex or age would be important to specialists using the method for prevention of CTS among working populations. Data from two large prospective studies were combined to allow for stratification of exposure-response models assessing the association between the TLV2018 and CTS by gender and age. Results show greater risk for women than men and for younger workers than older workers for TLV2018 values above the action limit. Although the TLV2018 is an effective surveillance tool for estimating increased risk of CTS with increasing exposure, these analyses show that such increase are not homogeneous across sex and age.
The effectiveness of adaptive training for stress inoculation in a simulated astronaut task
Authors: Finseth Tor , Dorneich Michael C. , Keren Nir , Franke Warren , Vardeman Stephen , Segal Jonathan , Deick Andrew , Cavanah Elizabeth , Thompson Kelly
Published: November 2021

Astronauts operate in an environment with multiple hazards that can develop into life-threatening emergency situations. Managing stress in emergencies may require cognitive resources and lead to diminishing performance. Stress training aims to maintain performance under stress by methodically increasing stressor levels to build inoculation against stress. An adaptive virtual reality (VR) training system was developed with real-time stress detection by using machine learning on psychophysiological responses. Using a VR simulation of a spaceflight emergency fire, stress classifications were used to trigger adaptations of the VR environmental stressors (e.g., smoke, alarms, flashing lights), with the goal of maintaining a manageable level of stress during training. Fifty-seven healthy subjects underwent task training over eight trials with adaptive training (adaptive, n=19); results were compared to trials with predetermined gradual increases in stressors (graduated, n=18), and with trials with constant low-level stressors (skill-only, n=20). Stress responses were measured through heart rate, heart rate variability (i.e., root mean squared of successive differences (RMSSD), low frequency to high frequency (LF/HF) ratio), and task performance (distance-from-fire). Heart rate decreased and RMSSD increased pre-post training for all experimental conditions. The LF/HF ratio decreased pre-post training for the adaptive condition, but not in the other conditions. Results suggests that all conditions had lower stress, but the adaptive condition was more successful. Task performance showed a marginal increase across trials for the adaptive condition. Preliminary results suggest that training with the adaptive stress system can prepare individuals for responding to stressors better than skill-only and graduated training.
Increasing physical activity among elementary school students during distance learning: The StandUp Kids Study
Authors: Okoro Judith I. , Ballen Brittany , Afterman Melissa , Harris Adamson Carisa , Robertson Michelle M.
Published: November 2021

Sedentary behaviour among primary school students has been associated with unfavourable health outcomes, which have been believed to be exacerbated by distance learning during the COVID-19 pandemic. We present the methodology used to design and develop interventions to increase physical activity in 4th grade students using a participatory, systems approach while online learning. Preliminary formative evaluation of training has indicated a positive reception by the stakeholders. This study highlights the importance of a systems approach to engage stakeholders in the betterment of our students’ health.
Requirements for Computational Approaches to Analyzing Resilience in Human-Machine Teams
Authors: Keller Jacob , IJtsma Martijn
Published: November 2021

Human-machine teams (HMTs) in complex work domains need to be able to adapt to variable and uncertain work demands. Computational modeling and simulation can provide novel approaches to the evaluation of HMTs performing complex joint activities, affording large-scale, quantitative analysis of team characteristics (such as system architecture and governance protocols) and their effects on resilience. Drawing from literature in resilience engineering, human-automation interaction, and cognitive systems engineering, this paper provides a theoretical exploration of the use of computational modeling and simulation to analyze resilience in HMTs. Findings from literature are summarized in a set of requirements that highlight key aspects of resilience in HMTs that need to be accounted for in future modeling and evaluation efforts. These requirements include a need to model HMTs as joint cognitive systems, the need to account for the interdependent nature of activity, the temporal dynamics of work, and the need to support formative exploration and inquiry. We provide a brief overview of existing modeling and simulation approaches to evaluating HMTs and discuss further steps for operationalizing the identified requirements.
Strategy Use in Automation-Aided Decision Making
Authors: Duncan-Reid Jackson , McCarley Jason S.
Published: November 2021

When human operators make signal detection judgments with assistance from an automated decision aid, they perform better than they could unaided but fail to reach optimal sensitivity. We investigated the decision strategies that produce this suboptimal performance. Participants ( N = 130) performed a two-response classification task that required them to mentally estimate the mean of a set of randomly sampled values each trial. The task was performed with and without assistance from a 93% reliable decision aid. Psychometric functions were fit to the classification data, and data were fit with two cognitive models of automation use. The first model assumed that participants made automation-aided judgments using a contingent criterion strategy, adjusting their response cutoff for yes vs. no responses following a cue from the aid. The second strategy, a discrete state model, assumed that participants made aided judgments by simply deferring to the aid on some proportion of trials. A measure of model fit favored the discrete-state process model, with parameter estimates indicating large individual differences in deferral rate between participants (range = 2% and 95%).
Differentiating Laparoscopic Skills of Trainees with Computer Vision Based Metrics
Authors: Deng Shiyu , Kulkarni Chaitanya , Wang Tianzi , Hartman-Kenzler Jacob , Barnes Laura E. , Henrickson Parker Sarah , Safford Shawn D. , Rajamohan Srijith , Lau Nathan K.
Published: November 2021

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.
Theoretical Considerations for Social Learning between a Human Observer and a Robot Model
Authors: Kim Boyoung , Phillips Elizabeth
Published: November 2021

Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.
Young Driver Emotions Relate To Their Intention To Engage In Cellphone Distraction
Authors: Hoseinzadeh Nooshabadi Mehdi , Vasquez Holland , Donmez Birsen
Published: November 2021

Young drivers’ disproportionate involvement in road crashes due to cellphone distractions calls for remedial programs. Development of such programs requires an understanding of the reasons behind engagement in distractions and identifying factors to leverage, such as emotions. This investigation sought to determine how young drivers’ intention to engage in cellphone distractions is associated with negative emotions. An online survey was administered to 99 young drivers (18 to 25). Participants rated their intention to engage in cellphone distractions while driving and their anticipation of negative emotions while doing so. Our results showed an association between anticipating negative emotions and the intention to engage in cellphone distractions while driving. There was no difference between males and females in their reported intention. However, females reported greater anticipation of negative emotions compared to males. These findings have implications for emotion-based manipulations aimed at reducing distracted driving among young drivers.
Human Theory of Mind Inference in Search and Rescue Tasks
Authors: Li Huao , Zheng Keyang , Lewis Michael , Hughes Dana , Sycara Katia
Published: November 2021

The ability to make inferences about other’s mental state is referred to as having a Theory of Mind (ToM). Such ability is the foundation of many human social interactions such as empathy, teamwork, and communication. As intelligent agents being involved in diverse human-agent teams, they are also expected to be socially intelligent to become effective teammates. To provide a feasible baseline for future social intelligent agents, this paper presents a experimental study on the process of human ToM reference. Human observers’ inferences are compared with participants’ verbally reported mental state in a simulated search and rescue task. Results show that ToM inference is a challenging task even for experienced human observers.
Nuclear Operating Experience Review and User Needs Assessment for a Risk-informed Predictive Maintenance Human System Interface
Authors: Ulrich Thomas A. , Mortenson Torrey
Published: November 2021

The U.S. commercial nuclear industry is facing increasing economic pressure due to low-cost alternatives such as natural gas combined-cycle combustion power plants and renewable energy technologies such as wind and solar. More efficient and intelligent operations and maintenance strategies can yield substantial economic gains through a proposed risk-informed predictive maintenance strategy. The strategy leverages plant process, maintenance, and vibration data with advanced data analytics to monitor and predict condition degradations in support of preventative maintenance. This paper documents the operating experience review of the existing monitoring and diagnostic process. A semi-structured series of interviews assessed current practices, tools, shortcomings, and anlaysts’ needs to support the development of a human system interface (HSI) for the advanced data analytics approach. The results of the interviews are reported as feature requirements for the proposed HSI system which is actively under development as part of this ongoing project.
A preliminary classification of different tasks that make use of uncertainty visualizations
Authors: Eskandar Mahtab , Giang Wayne C.W.
Published: November 2021

Individuals often struggle with tasks that involve uncertainty. Uncertainty visualizations are a type of cognitive aid that provides uncertainty information to help people with performing these tasks. However, the literature has shown that uncertainty visualizations differ in the extent they improve individuals’ task performance. We hypothesize that differences in the tasks can account for some of this variability. In this study, we aimed to create an initial classification of task types based on studies on uncertainty visualizations by reviewing a diverse set of recent research involving uncertainty visualizations. We classified the experimental tasks found in these papers into four groups: uncertainty assessment, forecasting, decision making, and metacognition. Then, we reviewed the result of the experiments in terms of the similarities and differences in the use of uncertainty visualizations within and between tasks. This classification serves as a starting point for further research into the effective design of visualizations of uncertainty.
Exploring Association between Perceived Usability of Dosimetry Quality Assurance Checklist and Perceived Cognitive Workload of Dosimetrists in Clinical Settings
Authors: Adapa Karthik , Mosaly Prithima , Yu Fei , Moore Carlton , Das Shiva , Mazur Lukasz
Published: November 2021

Usability and cognitive workload (CWL) are multidimensional constructs that describe user experience, predict performance, and inform system design. The relationship between the subjective measures of these constructs has not been adequately explored, especially in healthcare delivery settings where suboptimal usability of electronic health records and CWL of healthcare professionals are among the major contributing factors to medical errors. This study quantifies the perceived usability of a dosimetry quality assurance (QA) checklist and the perceived CWL of dosimetrists in radiation oncology clinical settings of an academic medical center and investigates the association between perceived usability and perceived CWL. Findings suggest that our institutional dosimetry QA checklist has suboptimal usability, but the associated CWL is acceptable. Further, the correlation analysis reveals that perceived usability and perceived CWL are non-overlapping constructs and may be jointly employed to reduce the risk of healthcare professionals committing medical errors.
Dynamics of Trust in Automation and Interactive Decision Making during Driving Simulation Tasks
Authors: Rodriguez Rodriguez Lucero , Bustamante Orellana Carlos , Landfair Jayci , Magaldino Corey , Demir Mustafa , Amazeen Polemnia G. , Metcalfe Jason S. , Huang Lixiao , Kang Yun
Published: November 2021

As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.
Human-Centered Modeling Applications In Intelligent Manufacturing Systems
Authors: Guillen Itzel , Montalvo Fernando
Published: November 2021

Industrial Internet of Things technologies, software algorithms, and advanced machine-learning systems are capable of extracting up-to-the-minute data from internal manufacturing industry processes which contain a wealth of useful and actionable information for various decision-making stages, including leadership-level strategic planning. While manufacturing data are often used to control quality, improve process efficiency, or to ensure that all manufacturing stations have the necessary tools and parts, this information also contains a wealth of data related to human factors that is not easily identified. By integrating human factors data into a causal modeling approach, this paper presents ways in which latent data can be used to aid in human-centered management of industrial systems. Information processing theory and causal modeling are presented as potential methods of incorporating human factors into modern data analytics. This research highlights ways to integrate human factors IIoT data into the overall company human factors’ planning and decisions.
Best Practices for Reducing Interface Errors in Electronic Medical Records
Authors: Lawton Paige , Ingraham Janel , Blickensderfer Beth
Published: November 2021

As Electronic Medical Records (EMR) become increasingly prevalent, the application of human factors principles is essential to facilitate efficiency and usability of these systems and, in turn, to reduce adverse patient outcomes due to user errors relating to the EMR. This paper describes five “best practices” found in the literature which aim to prevent error in the use of Electronic Medical Records. These practices are: Watermarking, Information Control and Management, Hybrid Systems, Cross-Checking Methodology, and Interface Modification. The paper describes each practice and examines the research underlying each approach. Although some practices may be easier to apply than others, they all merit further research and have potential for error prevention on a large scale.
Combined Model of Technology Use and Medical Adherence in eHealth Technology Implementation
Authors: Devkota Bijita , Montalvo Fernando , McConnell Daniel S. , Smither Janan A.
Published: November 2021

eHealth applications are expected to improve the effectiveness and efficiency of healthcare systems by providing improved medical information flow between medical providers and patients. Although the technology is expected to empower patients, lower treatment costs, and provide real-time collection of health data, individuals may be apprehensive about the use and efficacy of eHealth technologies. Medical professionals are often unaware of human factors technology acceptance or usability models which impact the use of medically focused technology, such as eHealth applications. Similarly, human factors professionals are often unaware of treatment adherence models which map the relationship of illness factors and individual differences to treatment protocols. The present paper presents a theoretical approach through which technology acceptance and usability models should be combined with medical treatment adherence models to ensure that eHealth applications are used properly and effectively.
“Welcome to your Daily Wellness Check”: The Proposed Evaluation of a SMS-based Conversational Agent for Managing Health and Wellbeing
Authors: Kettle Liam , Lee Yi-Ching
Published: November 2021

University students tend to sacrifice healthy lifestyles in favor of academic and financial gain. Additionally, the COVID-19 pandemic has led to online schooling and reduced access to healthcare resources. To mitigate these pressures, artificial intelligence conversational agents (CA) have addressed mental wellness in the general population but are yet to address physical well-being. Therefore, a CA that can address barriers and improve physical and mental well-being by cultivating healthy behaviors is desired. This research is designed to evaluate the feasibility and user experience of a text-based CA on addressing student barriers for a healthy lifestyle using evidence-based theories including the Theory of Planned Behavior and Motivational Interviewing over a two-week timeframe. Data collection has been paused due to the pandemic; results are expected to show positive impacts on well-being outcomes compared to an information-only control group and support the implementation of CAs to those with less accessibility to healthcare resources.
It’s All Fun and Games: The Role of Gamification in E-banking
Authors: Sethu Meenu , Nathan-Roberts Dan
Published: November 2021

Traditional banks and financial institutions have witnessed a profound transformation to electronic banking with the rise of the internet over the last two decades. However, most digital banking customers do not feel that the activity of managing their money and making online transactions is exciting or enjoyable. The gamification of e-banking systems is a novel approach for promoting customer engagement that is gaining popularity. This work reviews the factors influencing the adoption of e-banking and how gamification can be used to improve customer engagement, loyalty, and financial wellbeing. An exploration of the most extensively used game design elements in gamified e-banking applications suggests that the use of certain game mechanics and characteristics can be effective in creating enjoyable banking experiences. Based on this research, a set of guidelines is provided for designers and practitioners for introducing game principles in e-banking applications.
What Is the Relationship between Online Sex Workers and Cybersecurity?
Authors: Do Stephanie , Nathan-Roberts Dan
Published: November 2021

Although online sex work has become more accessible to people of all socio-economic statuses, labor practices and work safety have not improved since the widespread use of the internet. One way that we can help empower sex workers is to understand their motivations and experiences when using the internet. In a survey conducted by Sanders et al. (2017), the highest crime that 56.2% sex workers experienced was being threatened or harassed through texts, calls, and emails. Because there is no theory application to date on this marginalized group, three theories were proposed. This literature review highlights the need to explore why sex workers, as end-users, should be included in the user cybersecurity defense conversation, such as the cybercrimes that they face, their relationship with law enforcement, and what other factors affect their safety.
Individuation Based Usability of a Customized E-commerce Platform
Authors: Ford Steven , Schotz Jordan , Montalvo Fernando
Published: November 2021

The usability of interfaces is critical in commerce, as it can lead to increased productivity and revenue. However, employees often have different needs from a system based on their role within the company. Hancock, Hancock, and Warm (2009) suggested that applied scientists should embrace person-specific interactions as the world moves ever more toward individual customization. Magento is an open-source e-commerce platform that can be tailored to the needs of respective companies. In conjunction with a retail plant and flower sales company in Clearwater, Florida, we assessed the usability of a custom Magento interface, focusing on each employee’s individual needs in the organization using heuristic evaluation, usability assessment, and structured interviews.
Assessing Collaborative Explanations of AI using Explanation Goodness Criteria
Authors: Mamun Tauseef Ibne , Baker Kenzie , Malinowski Hunter , Hoffman Rober R. , Mueller Shane T.
Published: November 2021

Explainable AI represents an increasingly important category of systems that attempt to support human understanding and trust in machine intelligence and automation. Typical systems rely on algorithms to help understand underlying information about decisions and establish justified trust and reliance. Researchers have proposed using goodness criteria to measure the quality of explanations as a formative evaluation of an XAI system, but these criteria have not been systematically investigated in the literature. To explore this, we present a novel collaborative explanation system (CXAI) and propose several goodness criteria to evaluate the quality of its explanations. Results suggest that the explanations provided by this system are typically correct, informative, written in understandable ways, and focus on explanation of larger scale data patterns than are typically generated by algorithmic XAI systems. Implications for how these criteria may be applied to other XAI systems are discussed.
Paying Attention to Trust: Exploring the Relationship Between Attention Control and Trust in Automation
Authors: Textor Claire , Pak Richard
Published: November 2021

As automation continues to pervade people’s lives, it is critical to understand the reasons why some interactions are successful while others fail. Previous research attempting to explain this variability in HAI through individual differences in working memory has been mixed. Research in cognitive psychology has demonstrated the importance of attention control as a fundamental mechanism underlying higher-order cognition. In the realm of automation, early work has demonstrated a link between attention control and performance (Foroughi et al., 2019). The purpose of this exploratory study was to investigate the relationship between attention control and attitudes towards automation, particularly trust. Our results found attention control to be correlated with propensity to trust and negative attitudes towards robots. These results encourage further inquiry into the role of attention control in HAI.
Financial Orientated Heuristic Evaluation for Hand Tools 2021 HFES International Annual Meeting
Authors: Wang Zhonglun , Stone Richard , Kim Joseph , Baumann Steffen
Published: November 2021

The goal of purchasing hand tools is to improve financial outcomes for buyers. These financialgains can come from multiple aspects of hand tools, such as increased productivity, improved product quality, and decreased injury rate. Based on a literature review, current hand tool purchasingdecisions are frequently made by professionals without specialized scientific backgrounds; however, the hand tool selection methods they receive are solely based on ergonomic evaluations. Heuristic evaluation, created by Nielsen & Molich (1990), has been proven effective in discoveringusability issues quickly. Combined with the knowledge from hand tool evaluation literature andNielsen & Molich’s (1990) heuristic evaluation platform, two versions of financially orientatedhand tool heuristic evaluations were created: a buyer version and a seller version. The buyer versionhelps hand tool buyers make financially orientated purchasing decisions; the designer version helpsquickly assess the financial viability of the hand tool design under review.
Eye-Tracking Assistive Technology for Hands-Free Computer Navigation
Authors: McCurley Amelia , Nathan-Roberts Dan
Published: November 2021

Eye-tracking technologies for computer navigation assist those with mobility and verbal impairments in interacting with the world around them. Studies found that these technologies have a profound positive impact on the lives of these individuals and those who care for them. This proceeding explores the popular features of eye-tracking systems and the challenges they present. Specifically, this proceeding evaluates camera configurations, camera types, and command selections through the lenses of cost, ease of use, and comfort. Current and future design guidelines based on these criteria are recommended to best aid individuals with these impairments.
A Comparison of Presentation Mediums for the Study of Trust in Autonomous Vehicles
Authors: Garcia Katherine , Robertson Ian , Kortum Philip
Published: November 2021

The purpose of this study is to compare presentation methods for use in the validation of the Trust in Selfdriving Vehicle Scale (TSDV), a questionnaire designed to assess user trust in self-driving cars. Previous studies have validated trust instruments using traditional videos wherein participants watch a scenario involving an automated system but there are strong concerns about external validity with this approach. We examined four presentation conditions: a flat screen monitor with a traditional video, a flat screen with a 2D 180 video, an Oculus Go VR headset with a 2D 180 video, and an Oculus Go with a 3D VR video. Participants watched eight video scenarios of a self-driving vehicle attempting a right-hand tum at a stop sign and rated their trust in the vehicle shown in the video after each scenario using the TSDV and rated telepresence for the viewing condition. We found a significant interaction between the mean TSDV scores for pedestrian collision and presentation condition. The TSDV mean in the Headset 2D 180 condition was significantly higher than the other three conditions. Additionally, when used to view the scenarios as 3D VR videos, the headset received significantly higher ratings of spatial presence compared to the condition using a flatscreen a 2D video; none of the remaining comparisons were statistically significant. Based on the results it is not recommended that the headset be used for short scenarios because the benefits do not outweigh the costs.
Influence of the Big Five on Driving Frequency During the Early Phase of the COVID-19 Pandemic
Authors: Hickerson Kyle , Lee Yi-Ching
Published: November 2021

Our objective was to examine how the Big Five personality traits affected driving behavior during the early phase of the COVID-19 pandemic. Data was collected online via Amazon Mechanical Turk. Chi-Squared Tests with bootstrapping revealed a significant pandemic effect on driving frequency with a moderate effect size. However, insignificant results of the Big Five were found relative to the weekly driving frequency. Simple Correspondence Analysis was used to examine patterns between the Big Five and driving frequency graphically. Individuals high in Agreeableness, Openness, Conscientiousness, and Neuroticism were more likely to drive less frequently and therefore comply with isolation guidelines. However, there were conflicting results in Neuroticism. Some of the High and Medium Low groups were grouped closely with lower driving frequencies. To explain this result, there may be a mediating or moderating factor that was not captured in this analysis, which requires more research to examine.
Autonomous Vehicles, Children’s Mobility, And Family Perspective
Authors: Ayala Allegra , Lee Yi-Ching
Published: November 2021

This paper presents the current knowledge on ridership scenarios for autonomous vehicles and shuttles within the context of children’s mobility. Perspectives from parents, caregivers, and schools provide a unique use case that needs further attention from vehicle manufacturers and policy regulatory agencies. Social benefits of and barriers to adoption, willingness, and acceptance as well as hypothetical use scenarios are discussed from a family mobility perspective. Relevant accounts from other forms of automation are presented in parallel to highlight the challenges and opportunities for using autonomous and automated vehicles to enhance parent-child mobility practice. Future research opportunities are discussed to highlight the need to better understand barriers to adoption from parent, family, and school perspectives as well as potential practical contributions and real-world implications.