Thesis (M.S.)--University of Missouri-Columbia, 2003. / Accompany CD-ROM contains complete dissertation in Microsoft Word documents. Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Accompany CD-ROM contains complete dissertation in Microsoft Word documents. Typescript. Vita. Includes bibliographical references. Also available on the Internet.

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.

The desire to send humans to Mars will require a change in the way that extravehicular activity (EVA) is performed; in-space crews (including those within a vehicle or habitat monitoring others conducting EVA) will need to be more autonomous and that will require them to monitor large amounts of information in order to ensure crew safety and mission success. The amount of information to perceive and process will overwhelm unassisted intra-vehicular (IV) crewmembers, meaning that automation will need to be developed to support these crews on Mars while EVA is performed (Mishkin, Lee, Korth, & LeBlanc, 2007). This dissertation seeks to identify the information requirements for the performance of scientific EVA and determine which information streams will need to be allocated to in-space crew and which are the most effective streams to automate. The first study uses Mars rover operations as a homology—as defined by von Bertalanffy (1968)—to human scientific exploration. Mars rover operations personnel were interviewed using a novel method to identify the information requirements to perform successful science on Mars, how that information is used, and the timescales on which those information streams operate. The identified information streams were then related to potential information streams relevant to human exploration in order to identify potential function allocation or automated system development areas. The second study focused on one identified mission-critical information stream for human space exploration: monitoring astronaut status physiologically. Heart rate, respiration rate, and heart rate variability measurements were recorded from participants as they performed field science tasks (potentially tasks that are similar to those that will be performed by astronauts on Mars). A statistical method was developed to analyze this data in order to determine whether or not physiological responses to different tasks were statistically different, and whether any of those differences followed consistent patterns. A potential method to automate the monitoring of physiological data was also described. The results of this work provide a more detailed outline of the information requirements for EVA on Mars and can be used as a starting point for others in the exploration community to further develop automation or function allocation to support astronauts as they explore Mars.

<p dir="ltr">This research explores the potential of AR for lunar missions via the xEMU spacesuit. A market analysis of commercial off-the-shelf AR devices identifies technological trends and constraints that inform the architectural decisions for AR integration with the xEMU. User evaluations in simulated work environments ensure lunar informatics align with crew needs. Drawing insights from human-in-the-loop testing of COTS AR devices, qualitative test results underscore the importance of display optimization, occlusion management, and environmental considerations for enhancing the AR experience during lunar EVAs. Grounded in a task analysis from JETT3 analog testing, crew workflows and communication dynamics are baselined, underscoring the vital role of communication and collaboration. Integrating AR into the EVA work environment holds the potential to streamline decision-making, improve navigation, and enhance overall efficiency, but may come with unintended operational consequences. The human-centered approach prioritizes crew involvement, ensuring that technology remains a facilitator rather than an encumbering element in lunar exploration. The study's significance lies in advancing AR technology for lunar EVAs, guiding hardware design, and enabling seamless integration into the EVA work environment. AR holds promise in reshaping the human-technology relationship, empowering crew members, maximizing science output, and contributing to the next chapter in lunar exploration.</p>