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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. Ph. D.

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. Ph. D.

Advances in science and computing technology have accelerated the development and dissemination of a wide range of big data platforms such as bioinformatics into the biomedical and life sciences environments. Bioinformatics brings the promise of enabling life scientists to easily and effectively access large and complex data sets in new ways, thus promoting scientific discoveries by for example generating, validating, and refining hypotheses based on in silico analysis (performed on computer). Meanwhile, life scientists still face challenges in working with big data sets such as difficulties in data extraction and analyses arising from distributed and heterogeneous databases, user interface inconsistencies and discrepancies in results. Moreover, the interdisciplinary nature of modern science adds to significant gaps in scientists' performance caused by limited proficiency levels with bioinformatics resources and a lack of common language across different disciplines. Although developers of bioinformatics platforms are slowly beginning to move away from function-oriented software engineering approaches and towards to user-centered design approaches, they rarely consider users' value, and expectations that embrace different user contexts. Further, there is an absence of research that specifically aims to support the broad range of users from multiple fields of study, including 'wet' (lab-based) and dry' (computational) research communities. Therefore, the ultimate goal of this research is to investigate life scientists' user experiences with knowledge resources and derive design implications for delivering consistent user experiences across different user classes in order to better support data-intensive research communities. To achieve this research goal, we used the theory of distributed cognition as a framework for representing the dynamic interactions among end users and knowledge resources within computer-supported and -mediated environments. To be specific, this research focused on how online bioinformatics resources can be improved in order to both mitigate performance differences among the diverse user classes and better support distributed cognitive activities in data-intensive interdisciplinary research environments. This research consists of three parts: (1) understanding user experience levels with current bioinformatics resources and key determinants to encourage distributed cognitive activities, especially knowledge networking, (2) gaining in-depth understanding of scientists' insight generation behavior and human performance associated with individual differences (i.e., research roles and cognitive styles), and (3) identifying in-context usefulness, and barriers to make better use of bioinformatics resources in real working research contexts and derive design considerations to satisfactorily support positive user experiences. To achieve our research goals, we used a mixed-methods research approach that combines both quantitative (Study 1 and 2) and qualitative (Study 3) methods. First, as a baseline for subsequent studies, we conducted an empirical survey to examine 1) user experience levels with current bioinformatics resources, 2) important criteria to adequately support user requirements, 3) levels of knowledge networking (i.e., knowledge sharing and use) and relationship to users' larger set of distributed cognitive activities, and, 4) key barriers and enablers of knowledge networking. We collected responses from 179 scientists and our findings revealed that lack of integration, inconsistent results and user interfaces across bioinformatics resources, and perceived steep learning curves are current limitations to productive user experiences. Performance-related factors such as speed and responsiveness of resources and ease of use ranked relatively high as important criteria for bioinformatics resources. Our research also confirmed that source credibility, fear of getting scooped, and certain motivation factors (i.e., reciprocal benefit, reputation, and altruism) have an influence on scientists' intention to engage in distributed cognitive activities. Second, we conducted a laboratory experiment with a sample of 16 scientists in the broad area of bench and application sciences. We elicited 1) behavior characteristics, 2) insight characteristics, 3) gaze characteristics, and 4) human errors in relation to individual differences (i.e., research roles such as bench and application scientists, cognitive styles such as field-independent and dependent people) to identify whether human performance gaps exist. Our results (1) confirmed significant differences with respect to insight generation behavior and human performance depending on research roles, and (2) identified some relationships between scientists' cognitive styles and human performance. Third, we collected a rich set of qualitative data from 6 scientists using a longitudinal diary study and a focus group session. The specific objective of this study was to identify in-context usefulness and barriers to using knowledge resources in a real work context to subsequently derive focused design implications. For this work, we examined 1) the types of distributed cognitive activities participants performed, 2) the challenges and alternative actions they faced, 3) important criteria that influenced tasks, and 4) values to support distributed cognitive activities. Based on the empirical findings of this study, we suggest design considerations to support scientists' distributed cognitive activities from user experience perspectives. Overall, this research provides insights and implications for user interface design in order to support data-intensive interdisciplinary communities. Given the importance of today's knowledge-based interdisciplinary society, our findings can also serve as an impetus for accelerating a collaborative culture of scientific discovery in online biomedical and life science research communities. The findings can contribute to the design of online bioinformatics resources to support diverse groups of professionals from different disciplinary backgrounds. Consequently, the implications of these findings can help user experience professionals and system developers working in biomedical and life sciences who seek ways to better support research communities from user experience perspectives. Ph. D.

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. Ph. D.