As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate astronaut interactions with these systems. In line with NASA's Technology Roadmaps, 6.3 Human Health and Performance and 4.4 Human-System Interaction, the success of present and future autonomous technology in exploration spacecraft is ultimately dependent upon safe and efficient interaction with the human operator. Crew state may be affected by a number of variables including impairment, like fatigue or stress, attention failures, like channelized or diverted attention, and decision failures. I hypothesize that I can objectively classify continuous crew state using psychophysiological monitoring of an aerospace crew. Non-obtrusive monitoring techniques including heart rate and eye tracking will allow for minimalistic instrumentation that could be integrated into future spacecrafts and space habitats. Sensor testing and data training will be performed, ideally in a simulated or analogue space environment, to develop task-specific population-based classifiers of crew state. These population-based classifiers could then, in theory, be improved with crew member-specific classifiers. Ultimately, this research aims to develop a non-obtrusive sensor suite and algorithm to detect a suboptimal crew state. Once a crew state is determined, further research into specific countermeasures would be necessary to potentially alter the automation and improve crew state. Combining resources and expertise from Virginia Tech and NASA, through the visiting technologist experiences, would be extremely important for the proposed crew state monitoring and countermeasure research.