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A spacewalk, or extravehicular activity (EVA), is one of the most mission critical and physically and cognitively challenging tasks that crewmembers complete. With next-generation missions to the Moon and Mars, exploration EVA will challenge crewmembers in partial gravity environments with increased frequency, duration, and autonomy of operations. Given the distance from Earth, associated communication delays, and durations of exploration missions, there is a monumental shift in responsibility and authority taking place in spaceflight; moving from Earth-dependent to crew self-reliant. For the safety, efficacy, and efficiency of future surface EVAs, there is a need to better understand crew health and performance. With this knowledge, technology and operations can be designed to better support future crew autonomy. The focus of this dissertation is to develop and evaluate a psychophysiological monitoring tool to classify cognitive workload during an operationally relevant EVA task. This was completed by compiling a sensor suite of commercial wearable devices to record physiological signals in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. The approach employs supervised machine learning to recognize patterns in psychophysiological features across different psychological states. This relies on the ability to simulate, or induce, cognitive workload in order to label data for training the model. A virtual reality (VR) Translation Task was developed to control and quantify cognitive demands during an immersive, ambulatory EVA scenario. Participants walked on a passive treadmill while wearing a VR headset to move along a virtual lunar surface. They walked with constraints on time and resources, while simultaneously identifying and recalling waypoints in the scene. Psychophysiological features were extracted and labeled according to the task demands, i.e. high or low cognitive workload, for the novel Translation Task, as well as for the benchmark Multi-Attribute Task Battery (MATB). Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation. / Doctor of Philosophy / A spacewalk is one of the most important and physically and mentally challenging tasks that astronauts complete. With next-generation missions to the Moon and Mars, exploration spacewalks will challenge astronauts in reduced-weight environments (1/6 and 1/3 Earth's gravity) with longer, more frequent spacewalks and with less help from mission control. To keep astronauts safe while exploring there is a need to better understand astronaut health and performance (physical and mental) during spacewalks. With knowledge of how astronauts will respond to high workload and stressful events, we can plan missions and design tools that can best assist them during spacewalks on the Moon and Mars when help from Earth mission control is limited. Traditional tools of quantifying mental state are not suitable for real-time assessment during spacewalks. Current methods, including subjective surveys and performance-based computer tests, require time and attention to complete and cannot assess real-time operations. The focus of this dissertation is to create a psychophysiological monitoring tool to measure mental workload during a virtual reality (VR) spacewalk. Psychophysiological monitoring uses physiological measures, like heart rate and breathing rate, to predict psychological state, like high workload or stress. Physiological signals were recorded using commercial wearable devices in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. With machine learning, computer models can be trained to recognize patterns in physiological measures for different psychological states. Once a model is trained, it can be tested on new data to predict mental workload. To train and test the models, participants in the studies completed high and low workload versions of the VR task. The VR task was specifically designed for this study to simulate and measure performance during a mentally-challenging spacewalk scenario. The participants walked at their own pace on a treadmill while wearing a VR headset to move along a virtual lunar surface, while balancing their time and resources. They were also responsible for identifying and recalling flags along their virtual path. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.

This thesis research is involved with the development of new methodologies for enhancing the experimental use of computer simulations to optimize predicted human performance in a work domain. Using a computer simulation called Computer modeling Of Human Operator System Tasks (CoHOST) to test the concepts in this research, methods are developed that are used to establish confidence limits and significance thresholds by having the computer model self report its limits. These methods, along with experimental designs that are tailored to the use of computer simulation instead of human subject based research, are used in the CoHOST simulation to investigate the U.S. Army battalion level command and control work domain during combat conditions and develop recommendations about that domain based on the experimental use of CoHOST with these methodologies. Further, with the realization that analytical results showing strictly numerical data do not always satisfy the need for understanding by those who could most benefit from the analysis, the results are further interpreted in accordance with a team performance model and the CoHOST analysis results are mapped to it according to macroergonomic and team performance concepts. The CoHOST computer simulation models were developed based on Army needs stemming from the Persian Gulf war. They examined human mental and physical performance capabilities resulting from the introduction of a new command and control vehicle with modernized digital communications systems. Literature searches and background investigations were conducted, and the CoHOST model architecture was developed that was based on a taxonomy of human performance. A computer simulation design was implemented with these taxonomic based descriptors of human performance in the military command and control domain using the commercial programming language MicroSaint™. The original CoHOST development project developed results that suggested that automation alone does not necessarily improve human performance. The CoHOST models were developed to answer questions about whether human operators could operate effectively in a specified work domain. From an analytical point of view this satisfied queries being made from the developers of that work domain. However, with these completed models available, the intriguing possibility now exists to allow an investigation of how to optimize that work domain to maximize predicted human performance. By developing an appropriate experimental design that allows evaluative conditions to be placed on the simulated human operators in the computer model rather than live human test subjects, a series of computer runs are made to establish test points for identified dependent variables against specified independent variables. With these test points a set of polynomial regression equations are developed that describe the performance characteristics according to these dependent variables of the human operator in the work domain simulated in the model. The resulting regression equations are capable of predicting any outcome the model can produce. The optimum values for the independent variables are then determined that produce the maximum predicted human performance according to the dependent variables. The conclusions from the CoHOST example in this thesis complement the results of the original CoHOST study with the prediction that the primary attentional focus of the battalion commander during combat operations is on establishing and maintaining an awareness and understanding of the situational picture of the battlefield he is operating upon. Being able to form and sustain an accurate mental model of this domain is the predicted predominant activity and drives his ability to make effective decisions and communicate those decisions to the other members of his team and to elements outside his team. The potential specific benefit of this research to the Army is twofold. First, the research demonstrates techniques and procedures that can be used without any required modifications to the existing computer simulations that allow significant predictive use to be made of the simulation beyond its original purpose and intent. Second, the use of these techniques with CoHOST is developing conclusions and recommendations from that simulation that Army force developers can use with their continuing efforts to improve and enhance the ability of commanders and other decision makers to perform as new digital communications systems and procedures are producing radical changes to the paradigm that describes the command and control work domain. The general benefits beyond the Army domain of this research fall into the two areas of methodological improvement of simulation based experimental procedures and in the actual application area of the CoHOST simulation. Tailoring the experimental controls and development of interrogation techniques for the self-reporting and analysis of simulation parameters and thresholds are topics that bode for future study. The CoHOST simulation, while used in this thesis as an example of new and tailored techniques for computer simulation based research, has nevertheless produced conclusions that deviate somewhat from prevailing thought in military command and control. Refinement of this simulation and its use in an even more thorough simulation based study could further address whether the military decision making process itself or contributing factors such as development of mental models for understanding of the situation is or should be the primary focus of team decision makers in the military command and control domain. / Master of Science

21st century work environments are becoming more dynamic; they are fast-paced and require critical incidents to be dealt with in a shorter time frame. At the same time, in order for organizations to survive knowledge management (KM) systems need to be in place that allow organizations to learn from these incidents and use the knowledge gained to solve new problems. The knowledge life cycle consists of three phases: create, preserve, and disseminate. The knowledge life cycle also involves the transformation of knowledge from tacit to explicit, which is important to shift knowledge from the individual level to the organizational level; this represents a very important objective of KM. KM is not a domain on its own but intersects with other areas such as organizational learning, performance support, and communities of practice. Learning and performance support are among the concerns of practitioners in the sister fields of instructional design and technology (IDT) and human performance technology (HPT). Yet still, there are not many studies that examine KM through the lens of these professions. There is a need for knowledge to be accessible and for structures to be put in place to facilitate the knowledge life cycle. The purpose of this study was to explore how workers in a fast-paced environment go through the knowledge life cycle when dealing with critical incidents, and the factors that acted as driving and restraining forces. A single instrumental case study research design was used to study employees of a walk-in computer software help desk. The HPT model along with principles and procedures of critical incident technique were used to create a framework for data collection, which included interviews, a focus group session, and examination of extant data. Findings revealed that workers went through the knowledge life cycle by making internal and external connections and both organizational and individual factors impacted the flow of knowledge. A disconnection between available tools and work processes posed the greatest barrier to going through all the knowledge life cycle process. / Ph. D.

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. / Master of Science / Laparoscopic surgery is type of minimally invasive surgery which is being widely adopted. Skills required for performing laparoscopic surgeries are different than open surgeries. Hence, it is critical to ensure that adequate training and assessment is provided to surgeons. Eye-gaze tracking technology has made it possible to compute metrics that could be employed for skill assessment. These metrics are based on involuntary gaze behaviors and are independent of the nature of the surgical training task being performed. Hence, they may not be suitable for feedback during training. Metrics suitable for feedback are context dependent metrics which take into account the task based information. Experts tend to show look-ahead behavior while performing a task which can be quantified using context dependent metrics. This research presents a three stage methodology which facilitates computation of context dependent metrics and feed-forward metrics enabling identification of different skill levels in trainees. Applying this methodology to dataset of nine trainees with 499 practice trials, a total of six metrics were computed and a classification model was built to predict three identified skill level with 99% accuracy. This research is directly applicable to developing an automated system for laparoscopic training and assessment.

The fusion of the Internet with instructional design, and curricula delivery methods eliminated transactional distance in online learning. However, distance education (DE) in Nigeria has not aligned its pedagogy to the new reality in technology. The purposes of this non-experimental, predictive, validity study were to determine faculty and administrators' perceived barriers and concerns to online adoption and to validate the behavior engineering model (BEM) instrument. Ninety-six respondents from four public universities in Nigeria completed the questionnaires. Descriptive statistics and structural equation modeling (SEM) were used respectively, to assess barriers and concerns militating against faculty and administrators' online adoption, as well as validate the survey instruments. For faculty and administrators, incentive, motive, knowledge and skills influenced DE adoption. Except for age, all demographic factors influenced faculty's concerns. Gender was observed to influence administrators' concern. "Level of online use" influenced neither faculty nor administrators' concerns. Technographic characteristics influenced faculty, but not administrators.' Though the BEM instrument was reliable in measuring faculty and administrator's stages of concern, however, the 6-factor BEM, tested at the 95% significant level, did not give a good fit. The study contributes to positive social change by identifying gaps to effective DE implementation, and recommended the appropriate interventions to transform the DE experience for students and their universities. The study also proposed the framework to fast track Nigeria's vision and mission for DE.

In the past decade and more than any time before, new technologies have been broadly applied in various fields of interaction between human and machine. Despite many functionality studies, yet, how such technologies should be evaluated within the context of human computer interaction research remains unclear. This research aims at proposing a mechanism to evaluate/predict the design of user interfaces with their interacting components. At the first level of analysis, an original concept extracts the usability results of components, such as effectiveness, efficiency, adjusted satisfaction, and overall acceptability, for comparison in the fields of interest. At the second level of analysis, another original concept defines new metrics based on the level of complexity in interactions between input modality and feedback of performing a task, in the field of classical solid mechanics. Having these results, a set of hypotheses is provided to test if some common satisfaction criteria can be predicted from their correlations with the components of performance, complexity, and overall acceptability. In the context of this research, three multimodal applications are implemented and experimentally tested to study the quality of interactions through the proposed hypotheses: a) full-body gestures vs. mouse/keyboard, in a Box game; b) arm/hand gestures vs. three-dimensional haptic controller, in a Slingshot game; and c) hand/finger gestures vs. mouse/keyboard, in a Race game. Their graphical user interfaces are designed to cover some extents of static/dynamic gestures, pulse/continuous touch-based controls, and discrete/analog tasks measured. They are quantified based on a new definition termed index of complexity which represents a concept of effort in the domain of locomotion interaction. Single/compound devices are also defined and studied to evaluate the effect of user’s attention in multi-tasking interactions. The proposed method of investigation for usability is meant to assist human-computer interface developers to reach a proper overall acceptability, performance, and effort-based analyses prior to their final user interface design.

The human reliability analysis has been researched and developed for decades in different branches of industry: civil, chemical, petroleum, petrochemical, energy, among others. The increasingly strict legislation and the current public opinion factor due to accidents are even more crucial than the material losses and may order a company to bankruptcy. On the other hand beyond the corporate investment in risk prevention automation systems has been widely used both to reduce the exposure of people at risk and for financial gain with stabilizing and balancing processes, avoiding loss of raw materials and supplies , energy costs among others. However, this automation does not exempt people in their control arise some questions about the adequacy of the needs of operators, which factors most influence on your performance and what is the probability of human error during an emergency situation. To address these questions is necessary to use subjective variables without rigid boundaries that carry large uncertainties from the human knowledge and that in classical programming languages are not represented effectively. So Fuzzy logic has shown interesting results in the representation of these systems. In this work it was found that fuzzy logic is a powerful tool in determining factors that influence human performance and error probability based on experience of experts. / Conselho Nacional de Desenvolvimento Científico e Tecnológico / A análise de confiabilidade humana vem sendo pesquisada e desenvolvida durante décadas em indústrias de diferentes ramos: civil, química, petróleo, petroquímica de energia, entre outras. A legislação cada vez mais rígida e o atual fator de opinião público em decorrência de acidentes são ainda mais cruciais do que as perdas materiais e pode condenar uma empresa a falência. Por outro lado além dos investimentos das empresas na prevenção de risco a automatização dos sistemas vem sendo amplamente utilizada tanto para reduzir a exposição de pessoas ao risco quanto para obtenção de ganhos financeiros com a estabilização e balanceamento dos processos, evitando perda de matéria prima e insumos, gastos com energia entre outros. No entanto, esta automatização não dispensa pessoas em seu controle surgindo alguns questionamentos a respeito da adequação dos sistemas às necessidades dos operadores, quais fatores mais influenciam em seu desempenho e qual a probabilidade de um erro humano durante uma situação de emergência. Para sanar esses questionamentos é necessário o uso de variáveis subjetivas sem limites rígidos que carregam grandes incertezas provenientes do conhecimento humano e que nas linguagens de programação clássicas não são representadas de forma eficaz. Assim a lógica Fuzzy vem apresentando resultados interessantes na representação desses sistemas. Neste trabalho verificou-se que a lógica Fuzzy é uma ferramenta poderosa na determinação de fatores que influenciam o desempenho humano e a probabilidade de erro baseado na experiência de especialistas.