Psychophysiological monitoring of aerospace crew state

ABSTRACT

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember's time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator's cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

References

1. Andrea Bonarini, Luca Mainardi, Matteo Matteucci, Simone Tognetti, and Roberto Colombo. 2008. Stress Recognition in a Robotic Rehabilitation Task. In "Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics", A Workshop at ACM/IEEE HRI. Amsterdam, Netherlands, 41--48.
2. James C. Christensen, Justin R. Estepp, Glenn F. Wilson, and Christopher A. Russell. 2012. The effects of day-to-day variability of physiological data on operator functional state classification. NeuroImage 59, 1 (1 2012), 57--63.
3. Jr. Comstock, J. Raymond and Ruth J. Arnegard. 1992. The multi-attribute task battery for human operator workload and strategic behavior research. (1 1992). https://ntrs.nasa.gov/search.jsp?R=19920007912
4. Victor A. Convertino, Jeffrey T. Howard, Carmen Hinojosa-Laborde, Sylvain Cardin, Paul Batchelder, Jane Mulligan, Gregory Z. Grudic, Steven L. Moulton, and David B. MacLeod. 2015. Individual-specific, beat-to-beat trending of significant human blood loss: The compensatory reserve. Shock 44, 8 (2015), 27--32.
5. Deepan Das, Tanuka Bhattacharjee, Shreyasi Datta, Anirban Dutta Choudhury, Pratyusha Das, and Arpan Pal. 2017. Classification and Quantitative Estimation of Cognitive Stress from In-Game Keystroke Analysis using EEG and GSR. In IEEE Life Sciences Conference. IEEE, 286--291.
6. Angela R. Harrivel, Charles Liles, Chad L. Stephens, Kyle K. Ellis, Lance J. Prinzel, and Alan T. Pope. 2016. Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation. In AIAA Infotech @ Aerospace. 1--8.
7. Angela R. Harrivel, Chad L. Stephens, Robert J. Milletich, Christina M. Heinich, Mary Carolyn Last, Nicholas J. Napoli, Nijo Abraham, Lance J. Prinzel, Mark A. Motter, and Alan T. Pope. 2017. Prediction of Cognitive States during Flight Simulation using Multimodal Psychophysiological Sensing. In AIAA Information Systems-AIAA Infotech @ Aerospace. 1--10.
8. John-Dylan Haynes and Geraint Rees. 2006. Decoding mental states from brain activity in humans. Nature Reviews Neuroscience 7, 7 (7 2006), 523--534.
9. Matthew James Miller, Austin Claybrook, Jessica J Marquez, and Karen M Feigh. 2017. Operational Assessment of Apollo Lunar Surface Extravehicular Activity. NASA TPâĂŞ2017âĂŞ21, July (2017). https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170007261.pdf
10. Deba Pratim Saha, R. Benjamin Knapp, and Thomas L. Martin. 2017. Affective feedback in a virtual reality based intelligent supermarket. In ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers. 646--653.
11. Richard A Scheuring, David B Gillis, Josef Schmid, James M Duncan, Jeffrey R Davis, and Joseph D Novak. 2007. The Apollo Medical Operations Project: Recommendations to Improve Crew Health and Performance for Future Exploration Missions and Lunar Surface Operations. (2007). https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20070030109.pdf
12. Thomas Williams. 2016. Behavioral Health and Performance Laboratory Standard Measures. (2016). https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170005421.pdf
13. Glenn F. Wilson and Christopher A. Russell. 2003a. Operator Functional State Classification Using Multiple Psychophysiological Features in an Air Traffic Control Task. Human Factors: The Journal of the Human Factors and Ergonomics Society 45, 3 (9 2003), 381--389.
14. Glenn F. Wilson and Christopher A. Russell. 2003b. Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks. Human Factors: The Journal of the Human Factors and Ergonomics Society 45, 4 (2003), 635--644.