Browsing by Author "Lau, Nathan"

Now showing 1 - 20 of 27
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    Augmented Reality Pedestrian Collision Warning: An Ecological Approach to Driver Interface Design and Evaluation
    Kim, Hyungil (Virginia Tech, 2017-10-17)
    Augmented reality (AR) has the potential to fundamentally change the way we interact with information. Direct perception of computer generated graphics atop physical reality can afford hands-free access to contextual information on the fly. However, as users must interact with both digital and physical information simultaneously, yesterday's approaches to interface design may not be sufficient to support the new way of interaction. Furthermore, the impacts of this novel technology on user experience and performance are not yet fully understood. Driving is one of many promising tasks that can benefit from AR, where conformal graphics strategically placed in the real-world can accurately guide drivers' attention to critical environmental elements. The ultimate purpose of this study is to reduce pedestrian accidents through design of driver interfaces that take advantage of AR head-up displays (HUD). For this purpose, this work aimed to (1) identify information requirements for pedestrian collision warning, (2) design AR driver interfaces, and (3) quantify effects of AR interfaces on driver performance and experience. Considering the dynamic nature of human-environment interaction in AR-supported driving, we took an ecological approach for interface design and evaluation, appreciating not only the user but also the environment. The requirement analysis examined environmental constraints imposed on the drivers' behavior, interface design translated those behavior-shaping constraints into perceptual forms of interface elements, and usability evaluations utilized naturalistic driving scenarios and tasks for better ecological validity. A novel AR driver interface for pedestrian collision warning, the virtual shadow, was proposed taking advantage of optical see-through HUDs. A series of usability evaluations in both a driving simulator and on an actual roadway showed that virtual shadow interface outperformed current pedestrian collision warning interfaces in guiding driver attention, increasing situation awareness, and improving task performance. Thus, this work has demonstrated the opportunity of incorporating an ecological approach into user interface design and evaluation for AR driving applications. This research provides both basic and practical contributions in human factors and AR by (1) providing empirical evidence furthering knowledge about driver experience and performance in AR, and, (2) extending traditional usability engineering methods for automotive AR interface design and evaluation.
  • Automating context dependent gaze metrics for evaluation of laparoscopic surgery manual skills
    Deng, Shiyu; Kulkarni, Chaitanya; Parker, Sarah J.; Barnes, Laura E.; Wang, Tianzi; Hartman-Kenzler, Jacob; Safford, Shawn; Lau, Nathan (2022-03)
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    Changes in forklift driving performance and postures among novices resulting from training using a high-fidelity virtual reality simulator: An exploratory study
    Islam, Md Shafiqul; Zahabi, Saman Jamshid Nezhad; Kim, Sunwook; Lau, Nathan; Nussbaum, Maury A.; Lim, Sol (Elsevier, 2024-11-01)
    Virtual reality (VR) has emerged as a promising tool for training. Our study focused on training for forklift driving, to address an ongoing worker shortage, and the unknown impact of repeated VR training on task performance and kinematic adaptations. We trained 20 novice participants using a VR forklift simulator over two days, with two trials on each day, and including three different driving lessons of varying difficulties. Driving performance was assessed using task completion time, and we quantified kinematics of the head, shoulder, and lumbar spine. Repeated training reduced task completion time (up to ∼29.8% of initial trial) and decreased both kinematic variability and peak range of motion, though these effects were larger for lessons requiring higher precision than simple driving maneuvers. Our results highlight the potential of VR as an effective training environment for novice drivers and suggest that monitoring kinematics could help track skill acquisition during such training.
  • Cognitive Workload of Novice Forklift Truck Drivers in VR-based Training
    Jamshid Nezhad Zahabi, Saman; Shafiqul Islam, Md; Kim, Sunwook; Lau, Nathan; Nussbaum, Maury A.; Lim, Sol (SAGE, 2023-10-19)
    There is increasing use of Virtual Reality (VR) to train forklift truck operators but a lack of sufficient understanding of how cognitive workload changes with respect to different task demands in VR-based training. In this study, 19 novice participants completed three forklift driving lessons with varying difficulty levels (low, medium, and high) using a VR simulator. To examine the effect of repeated training on cognitive workload, two sessions were repeated by participants using the same procedures. Cognitive workload was assessed with objective (electroencephalogram [EEG] activity) and subjective (NASA-TLX) measurements. EEG theta power and NASA-TLX (mental workload) scores were significantly higher for high than low difficulty levels. However, both EEG and NASA-TLX responses were reduced with repeated training in the second session. These findings highlight the effectiveness of EEG in continuous monitoring of workload variation caused by task difficulty and implementing training programs to moderate cognitive workload for forklift operators.
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    Collaborative Multi-Robot Multi-Human Teams in Search and Rescue
    Williams, Ryan K.; Abaid, Nicole; McClure, James; Lau, Nathan; Heintzman, Larkin; Hashimoto, Amanda; Wang, Tianzi; Patnayak, Chinmaya; Kumar, Akshay (2022-04-30)
    Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that yields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy.
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    Context Dependent Gaze Metrics for Evaluation of Laparoscopic Surgery Manual Skills
    Kulkarni, Chaitanya Shashikant (Virginia Tech, 2021-06-10)
    With the growing adoption of laparoscopic surgery practices, high quality training and qualification of laparoscopic skills through objective assessment has become critical. While eye-gaze and instrument motion analyses have demonstrated promise in producing objective metrics for skill assessment in laparoscopic surgery, three areas deserve further research attention. First, most eye-gaze metrics do not account for trainee behaviors that change the visual scene or context that can be addressed by computer vision. Second, feedforward control metrics leveraging on the relationship between eye-gaze and hand movements has not been investigated in laparoscopic surgery. Finally, eye-gaze metrics have not demonstrated sensitivity to skill progressions of trainees as the literature has focused on differences between experts and novices although feedback on skill acquisition is most useful for trainees or educators. To advance eye-gaze assessment in laparoscopic surgery, this research presents a three-stage gaze based assessment methodology to provide a standardized process for generating context-dependent gaze metrics and estimating the proficiency levels of medical trainees on surgery. The three stages are: (1) contextual scene analysis for segmenting surgical scenes into areas of interest, (2) compute context dependent gaze metrics based on eye fixation on areas of interest, and (3) defining and estimating skill proficiency levels with unsupervised and supervised learning, respectively. This methodology was applied to analyze 499 practice trials by nine medical trainees practicing the peg transfer task in the Fundamental of Laparoscopic Surgery program. The application of this methodology generated five context dependent gaze and one tool movement metrics, defined three proficiency levels of the trainees, and developed a model predicting proficiency level of a participant for a given trial with 99% accuracy. Further, two of six metrics are completely novel, capturing feed-forward behaviors in the surgical domain. The results also demonstrated that gaze metrics could reveal skill levels more precisely than between experts and novices as suggested in the literature. Thus, the metrics derived from the gaze based assessment methodology also shows high sensitive to trainee skill levels. The implication of this research includes providing automated feedback to trainees on where they have looked during practice trial and what skill proficiency level attained after each practice trial.
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    Design of Gaze-Based Alarm Acknowledgement by Parameter Characteristics
    Herdt, Katherine; Lau, Nathan; Hilderbrant, Michael; Le, Tai; LeBlanc, Katya (Springer, 2022-06-16)
    Alarms in industrial control rooms are defined by their ability to alert an operator of abnormal events that require prompt response. However, when vigilant, operators may anticipate upcoming alarms, rendering those alarms less informative if not a nuisance. Three gaze-based alarm acknowledgement methods were designed by estimating operator awareness based on their eye fixations on the parameter/area of interest and parameter behavior shortly before the alarm. The three designs differed in acknowledging the types of parameter behaviors, which could be: a) near the alarm threshold, b) fluctuating drastically, or c) trending towards an alarm threshold. These three parameter behaviors correlate with increased visual sampling, which suggests higher operator awareness or expectation of alarms. In a simulator study comparing the three gaze-based acknowledgement methods against no gaze acknowledgement, 24 participants completed 24 trials of alarm monitoring task while maintaining a single parameter within a predefined range. Analysis of variance revealed that usability ratings were higher for conditions with than without gaze acknowledgements, demonstrating promise for this alarm management approach.
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    Effects of a Driver Monitoring System on Driver Trust, Satisfaction, and Performance with an Automated Driving System
    Vasquez, Holland Marie (Virginia Tech, 2016-01-27)
    This study was performed with the goal of delineating how drivers' interactions with an Automated Driving System were affected by a Driver Monitoring System (DMS), which provided alerts to the driver when he or she became inattentive to the driving environment. There were two specific research questions. The first was centered on addressing how drivers' trust and satisfaction with an Automated Driving System was affected by a DMS. The second was centered on addressing how drivers' abilities to detect changes in the driving environment that required intervention were affected by the presence of a DMS. Data were collected from fifty-six drivers during a test-track experiment with an Automated Driving System prototype that was equipped with a DMS. DMS attention prompt conditions were treated as the independent variable and trust, satisfaction, and driver performance during the experimenter triggered lane drifts were treated as dependent variables. The findings of this investigation suggested that drivers who receive attention prompts from a DMS have lower levels of trust and satisfaction with the Automated Driving System compared to drivers who do not receive attention prompts from a DMS. While the DMS may result in lower levels of trust and satisfaction, the DMS may help drivers detect changes in the driving environment that require attention. Specifically, drivers who received attention prompts after 7 consecutive seconds of inattention were 5 times more likely to react to a lane drift with no alert compared to drivers who did not receive attention prompts at all.
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    Effects of Intersection Lighting Design on Driver Visual Performance, Perceived Visibility, and Glare
    Bhagavathula, Rajaram (Virginia Tech, 2016-01-12)
    Nighttime intersection crashes account for nearly half of all the intersection crashes, making them a major traffic safety concern. Although providing lighting at intersections has proven to be a successful countermeasure against these crashes, existing approaches to designing lighting at intersections are overly simplified. Current standards are based on recommending lighting levels, but do not account for the role of human vision or vehicle headlamps or the numerous pedestrian-vehicle conflict locations at intersections. For effective intersection lighting design, empirical evidence is required regarding the effects of lighting configuration (part of the intersection illuminated) and lighting levels on nighttime visibility. This research effort had three goals. The first was to identify an intersection lighting design that results in the best nighttime visibility. The second goal was to determine the effect of illuminance on visual performance at intersections. The third goal was to understand the relationships between object luminance, contrast, and visibility. To achieve these goals, three specific configurations were used, that illuminated the intersection approach (Approach), intersection box (Box), and both the intersection approach and box (Both). Each lighting configuration was evaluated under five levels of illumination. Visibility was assessed both objectively (visual performance) and subjectively (perceptions of visibility and glare). Illuminating the intersection box led to superior visual performance, higher perceived visibility, and lower perceived glare. For this same configuration, plateaus in visual performance and perceived visibility occurred between 8 and 12 lux illuminance levels. A photometric analysis revealed that the Box lighting configuration rendered targets in sufficient positive and negative contrasts to result in higher nighttime visibility. Negatively contrast targets aided visual performance, while for targets rendered in positive contrast visual performance was dependent on the magnitude of the contrast. The relationship between pedestrian contrast and perceived pedestrian visibility was more complex, as pedestrians were often rendered in multiple contrast polarities. These results indicate that Box illumination is an effective strategy to enhance nighttime visual performance and perceptions of visibility while reducing glare, and which may be an energy efficient solution as it requires fewer luminaires.
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    Evaluation of biofeedback components for the management of acute stress in healthcare
    Kennedy-Metz, Lauren Rose (Virginia Tech, 2018-11-27)
    Medical error is the third leading cause of death in the United States, with surgery being a critical area for improvement. Of particular interest for this dissertation is understanding and mitigating the impact of acute stress experienced by surgeons. Previous research demonstrates the detrimental effects mismanaged acute stress can have on cognitive performance integral in optimal surgical practice. Biofeedback consists of objectively monitoring signs of stress, presenting physicians with their own physiological output in real time. Introducing appropriate, targeted coping mechanisms when they are most needed may facilitate behavioral adjustments in the face of acute stress. The goal of this dissertation research was to evaluate the potential benefit of biofeedback and coping instructions, measured by reduced perceived and physiological stress, and improved task performance. In the first study, college students participated in a first-person shooter videogame while receiving visual coping instructions. Instructions that were presented at moments of elevated stress improved downstream physiology compared to randomly administered instruction, and the presence of coping instructions was more beneficial than their absence at highly stressful times. In the second study, I adapted and validated a computer-based task to focus on components of workload experienced by physicians. This study yielded one high-stress and one-low stress version of a more demographic-appropriate task. In the final study, medical students and residents completed this task. The independent variables tested included a visual biofeedback interface, intermittent auditory coping instructions, and/or brief training on stress management and emotional intelligence. Results from this study showed that despite high cognitive workload experienced by participants receiving both biofeedback and coping instructions, performance across stress levels was indistinguishable, and physiological indicators of stress immediately following discrete coping instructions was reflective of decreased stress. Taken together, the results of these studies validate the generation of a new lab-based task to induce stress among healthcare providers, and the physiological and performance benefits associated with physiologically-based coping instructions. Future work should investigate how these concepts can be tailored towards surgical workflow with feedback modality in mind, extended to teams, and/or scaled up to higher levels of fidelity to better capture the work environment.
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    Examining Senior Drivers Adaptation to Mixed Level Automated Vehicles: A Naturalistic Study
    Liang, Dan; Antin, Jonathan F.; Lau, Nathan; Stulce, Kelly E.; Baker, Stephanie Ann; Wotring, Brian (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
    Advances in the development of advanced vehicle technologies (AVTs), such as blind spot alerts, lane keep assist,lane alert, and adaptive cruise control, can benefit senior drivers by reducing exposure to hazards andcompensating for diminished cognitive abilities sometimes seen in this population. However, the degree to whichsuch benefits can be realized in this vulnerable population depends largely on the degree to which senior driverswill accept, adopt, and adapt to these features. This study investigated how 18 seniors, aged 70–79, accepted,trusted, and used mixed-function AVTs when provided an AVT-equipped vehicle to drive as they desired for a 6-week period. Researchers assessed attitudes and the effect of exposure via before-and-after exposure surveys, briefweekly check-in surveys during the driving exposure period, and focus group sessions conducted after theconclusion of the driving exposure period. Analyses revealed that seniors prefer technologies that inform, such asblind spot alert, over those that assert independent control over the vehicle, such as lane keep assist. Increasedconfidence in and willingness to use AVTs correlated positively with exposure, with adequate time for orientationand appropriate user documentation emerging as key factors determining senior drivers’ acceptance.
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    Examining senior drivers' attitudes toward advanced driver assistance systems after naturalistic exposure
    Liang, Dan; Lau, Nathan; Baker, Stephanie Ann; Antin, Jonathan F. (Oxford University Press, 2020-01-01)
    Background and Objectives: The increasing number of senior drivers may introduce new road risks due to age-related declines in physical and cognitive abilities. Advanced driver assistance systems (ADAS) have been proposed as solutions to minimize age-related declines, thereby increasing both senior safety and mobility. This study examined factors that influence seniors' attitudes toward adopting ADAS after significant exposure to the technology in naturalistic settings. Research Design and Methods: This study recruited 18 senior drivers aged 70-79 to drive vehicles equipped with ADAS for 6 weeks in their own environments. Afterward, each participant was enrolled in 1 of the 3 focus group sessions to discuss their changes in attitude toward ADAS based on their driving experiences. We applied structural topic modeling (STM) on the focus group transcripts to reveal key topics deemed important to seniors. Results: STM revealed 5 topics of importance for seniors. In order of prevalence, these were (i) safety, (ii) confidence concerning ADAS, (iii) ADAS functionality, (iv) user interface/usability, and (v) non-ADAS-related features. Based on topics and associated keywords, seniors perceived safety improvement with ADAS but expressed concerns about its limitations in coping with adverse driving conditions. Experience and training were suggested for improving seniors' confidence in ADAS. Blind spot alert and adaptive cruise control received the most discussion regarding perceived safety and comfort. Discussion and Implications: This study indicated that promoting road safety for senior drivers through ADAS is feasible. Acceptance and appropriate use of ADAS may be supported through intuitive and senior-friendly user interfaces, in-depth training programs, and owner's manuals specifically designed and tested for senior drivers.
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    Examining senior drivers’ acceptance to advanced driver assistance systems
    Liang, Dan; Antin, Jonathan F.; Lau, Nathan (2019-09-10)
    Advanced driver assistance systems (ADAS) can help maintain seniors’ safety and mobility with their decline in cognitive and physical capabilities. An early step of investigating the adoption and merits of ADAS for senior drivers is examining the factors that influence senior drivers’ acceptance of the technology. This paper presents our modeling effort on the acceptance of 18 senior drivers towards adaptive cruise control (ACC) and lane control features after six weeks of naturalistic driving with study vehicles. Adapting the Technology Acceptance Model (TAM), our model is built on questionnaire data on perceived usefulness (PU), perceived ease of use (PEoU), usebased trust (T) and perceived satisfaction (PS) in predicting behavioral intention to use (BIU) ADAS. Two major findings in our modeling effort are that (i) perceived ease of use has significant influence on trust and (ii) perceived satisfaction has significant influence on behavioral intention to use.
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    Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data
    Liang, Dan; Antin, Jonathan F.; Lau, Nathan; Stulce, Kelly E. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    The study examined whether advanced driver assistance systems (ADAS) can benefit the mobility and driving performance of senior drivers. Two groups of driving data, collected separately from two naturalistic driving projects, were examined. The Second Strategic Highway Research Program and the Examining Seniors’ Adaptation to Mixed Function Automated Vehicles project databases were used to compare measurements of mobility and driving performance. Mobility analyses did not yield significant differences between seniors who drove conventional vehicles and those who drove ADAS-equipped vehicles. As to driving performance, three analyses were conducted to address different research interests. Results indicated that ADAS-equipped vehicles influence seniors’ driving performance both in positive as well as negative ways. Seniors generally displayed better speed management performance while driving the ADAS-equipped vehicles. Using adaptive cruise control (ACC) may help seniors reduce the frequency and level of higher g-force accelerations. However, poorer lateral control performance was observed during trips where ACC was used. The study is the first to investigate the influence of ADAS on the mobility and driving performance of seniors in real-world traffic and road environments.
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    Forklift Driving Performance of Novices with Repeated VR-based Training
    Islam, Md Shafiqul; Jamshid Nezhad Zahabi, Saman; Kim, Sunwook; Lau, Nathan; Nussbaum, Maury A.; Lim, Sol (SAGE, 2023-10-19)
    Virtual reality (VR) has emerged as a promising tool for training novice forklift drivers, but temporal patterns of such improvements are largely unknown. We trained 19 novice participants using an order-picker VR simulator on a selected driving lesson. In two sessions, participant driving performance was assessed using task completion time and kinematics of the head, shoulder, and lumbar spine via inertial measurement units (IMUs). Completion time and head flexion/movement decreased significantly (up to 22.4% and 31.5%, respectively). The observed changes in head motion (flexion/extension) indicate an initial adjustment period to prepare a mental model of the driving task and the control panel, which was also adapted over repeated trials. One implication of our results is that reduced head flexion/extension could be used as an indication of a novice driver’s improved skill during the early stages of training, in terms of familiarizing themselves with vehicle control and the vehicle control panel.
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    The Impact of Sleep Disorders on Driving Safety - Findings from the SHRP2 Naturalistic Driving Study
    Liu, Shuyuan (Virginia Tech, 2017-06-15)
    This study is the first examination on the association between seven types of sleep disorder and driving risk using large-scale naturalistic driving study data involving more than 3,400 participants. Regression analyses revealed that females with restless leg syndrome or sleep apnea and drivers with insomnia, shift work sleep disorder, or periodic limb movement disorder are associated with significantly higher driving risk than other drivers without those conditons. Furthermore, despite a small number of observations, there is a strong indication of increased risk for narcoleptic drivers. The findings confirmed results from simulator and epidemiological studies that the driving risk increases amongst people with certain types of sleep disorders. However, this study did not yield evidence in naturalistic driving settings to confirm significantly increased driving risk associated with migraine in prior research. The inconsistency may be an indication that the significant decline in cognitive performance among drivers with sleep disorders observed in laboratory settings may not nessarily translate to an increase in actual driving risk. Further research is necessary to define how to incentivize drivers with specific sleep disorders to balance road safety and personal mobility.
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    Informing Design of In-Vehicle Augmented Reality Head-Up Displays and Methods for Assessment
    Smith, Martha Irene (Virginia Tech, 2018-08-23)
    Drivers require a steady stream of relevant but focused visual input to make decisions. Most driving information comes from the surrounding environment so keeping drivers' eyes on the road is paramount. However, important information still comes from in-vehicle displays. With this in mind, there has been renewed recent interest in delivering driving in-formation via head-up display. A head-up display (HUD) can present an image directly on-to the windshield of a vehicle, providing a relatively seamless transition between the display image and the road ahead. Most importantly, HUD use keeps drivers' eyes focused in the direction of the road ahead. The transparent display coupled with a new location make it likely that HUDs provide a fundamentally different driving experience and may change the way people drive, in both good and bad ways. Therefore, the objectives of this work were to 1) understand changes in drivers' glance behaviors when using different types of displays, 2) investigate the impact of HUD position on glance behaviors, and 3) examine the impact of HUD graphic type on drivers' behaviors. Specifically, we captured empirical data regarding changes in driving behaviors, glance behaviors, reported workload, and preferences while driving performing a secondary task using in-vehicle displays. We found that participants exhibited different glance behaviors when using different display types, with participants allocating more and longer glances towards a HUD as compared to a traditional Head-Down Display. However, driving behaviors were not largely affected and participants reported lower workload when using the HUD. HUD location did not cause large changes in glance behaviors, but some driving behaviors were affected. When exam-ining the impact of graphic types on participants, we employed a novel technique for ana-lyzing glance behaviors by dividing the display into three different areas of interest relative to the HUD graphic. This method allowed us to differentiate between graphic types and to better understand differences found in driving behaviors and participant preferences than could be determined with frequently used glance analysis methods. Graphics that were fixed in place rather than animated generally resulted in less time allocated to looking at the graphics, and these changes were likely because the fixed graphics were simple and easy to understand. Ultimately, glance and driving behaviors were affected at some level by the display type, display location, and graphic type as well as individual differences like gender and age.
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    Localized Muscle Fatigue: Theoretical and Practical Aspects in Occupational Environments
    Rashedi, Ehsan (Virginia Tech, 2016-01-15)
    Localized muscle fatigue (LMF) is a complex, multifactorial phenomenon that involves exercise-induced decrements in the ability to generate force or power. LMF can adversely affect performance and may increase the risk of work-related musculoskeletal disorders (WMSDs), and is thus of contemporary occupational relevance. Despite considerable progress in understanding and predicting muscle fatigue, there are many uncertainties and unresolved issues that are principally associated with the physiological complexity of LMF and the diverse mechanisms that underlie LMF development. This research thus aimed to address some of the theoretical and practical issues related to muscle fatigue and recovery. Regarding the theoretical aspects, two specific muscle fatigue models (MFMs) were directly compared and some important differences in their predictions were identified. These differences were used, in part, as a basis for developing testable hypotheses and designing associated experiments. Further theoretical evaluations were conducted to explore the sensitivity of these models to the model parameters and their ability to predict endurance time in both prolonged and intermittent exertions. Sensitivity to inherent model parameters was quantified, which was relatively high in conditions involving lower to moderate levels of effort. Further assessments indicated substantial variability related to model recovery parameters, which might be related to the inability of these MFMs in simulating the recovery process. From a practical viewpoint, the effect of cycle time on the development and consequences of LMF was determined during intermittent isometric exertions. A shorter cycle time led to less fatigue development as reflected by rates of change in perceived discomfort, performance, and muscle capacity. Lastly, the dependency of muscle recovery on these different histories of fatiguing muscle contractions was explored. How a muscle recovers appeared to depend only on the state from which it starts to recover, though not the exertion history that led to that state. In summary, results of these studies may help in enhancing our understanding of fatigue and recovery processes, and in improving existing models of muscle fatigue and recovery. More accurate predictions of LMF development may help in enhancing muscle performance and in reducing the risk of musculoskeletal injuries and their associated healthcare costs.
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    Modeling of older adults’ driving exposure and avoidance using objective driving data in a naturalistic driving study
    Liang, Dan; Lau, Nathan; Antin, Jonathan F. (Elsevier, 2022-09-01)
    Older adults in the United States rely heavily on driving their own vehicles to commute to work, shop for groceries, and access public services. To effectively help older adults maintain mobility and independence, we need to better understand how the cognitive, visual functioning, and health declines influence their tendency to self-restrict their driving. The objective of this study is to develop a causal model to examine the effects of age, gender, household status (specifically living alone), physical, cognitive, visual abilities, and health status on older adults’ driving mobility in terms of driving exposure and avoidance. Driving exposure was measured by actual driving data, whereas driving avoidance was assessed by both self-report data and actual driving exposure to challenging situations. Structural equation modeling was used to analyze data collected in the Second Strategic Highway Research Program Naturalistic Driving Study for establishing relationships between the selected factors and mobility. The structural equation model included a total of 794 participants aged 65 and over (367 or 46.22% females and 427 or 53.78% males). Results indicate that poorer health is associated with less driving exposure; deteriorating cognitive and physical capabilities are associated with more self-reported driving avoidance and less actual driving in challenging situations; visual function is associated with self-reported avoidance; living alone is associated with higher driving exposure in general as well as in challenging situations; self-reported driving avoidance of challenging situations has a negative association with actual driving in those same situations. The final model could be applied to predict older adults’ mobility changes according to their age, gender, household status, as well as their visual, physical, cognitive and health status.
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    Multiscale Quantitative Analytics of Human Visual Searching Tasks
    Chen, Xiaoyu (Virginia Tech, 2021-07-16)
    Benefit from the recent advancements of artificial intelligence (AI) methods, industrial automation has replaced human labors in many tasks. However, humans are still placed in the central role when visual searching tasks are highly involved for manufacturing decision-making. For example, highly customized products fabricated by additive manufacturing processes have posed significant challenges to AI methods in terms of their performance and generalizability. As a result, in practice, human visual searching tasks are still widely involved in manufacturing contexts (e.g., human resource management, quality inspection, etc.) based on various visualization techniques. Quantitatively modeling the visual searching behaviors and performance will not only contribute to the understanding of decision-making process in a visualization system, but also advance AI methods by incubating them with human expertise. In general, visual searching can be quantitatively understood from multiple scales, namely, 1) the population scale to treat individuals equally and model the general relationship between individual's physiological signals with visual searching decisions; 2) the individual scale to model the relationship between individual differences and visual searching decisions; and 3) the attention scale to model the relationship between individuals' attention in visual searching and visual searching decisions. The advancements of wearable sensing techniques enable such multiscale quantitative analytics of human visual searching performance. For example, by equipping human users with electroencephalogram (EEG) device, eye tracker, and logging system, the multiscale quantitative relationships among human physiological signals, behaviors and performance can be readily established. This dissertation attempts to quantify visual searching process from multiple scales by proposing (1) a data-fusion method to model the quantitative relationship between physiological signals and human's perceived task complexities (population scale, Chapter 2); (2) a recommender system to quantify and decompose the individual differences into explicit and implicit differences via personalized recommender system-based sensor analytics (individual scale, Chapter 3); and (3) a visual language processing modeling framework to identify and correlate visual cues (i.e., identified from fixations) with humans' quality inspection decisions in human visual searching tasks (attention scale, Chapter 4). Finally, Chapter 5 summarizes the contributions and proposes future research directions. The proposed methodologies can be readily extended to other applications and research studies to support multi-scale quantitative analytics. Besides, the quantitative understanding of human visual searching behaviors performance can also generate insights to further incubate AI methods with human expertise. Merits of the proposed methodologies are demonstrated in a visualization evaluation user study, and a cognitive hacking user study. Detailed notes to guide the implementation and deployment are provided for practitioners and researchers in each chapter.