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Psychophysiological monitoring of aerospace crew state

Published: 09 September 2019 Publication History

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.

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  • (2024)Towards a Radiation-Tolerant Display System2024 IEEE Space Computing Conference (SCC)10.1109/SCC61854.2024.00020(122-130)Online publication date: 15-Jul-2024
  • (2022)Importance of Testing with Independent Subjects and Contexts for Machine-Learning Models to Monitor Construction Workers’ Psychophysiological ResponsesJournal of Construction Engineering and Management10.1061/(ASCE)CO.1943-7862.0002341148:9Online publication date: Sep-2022
  • (2020)Assessing Cognitive Performance Using Physiological and Facial FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118114:3(1-41)Online publication date: 4-Sep-2020

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cover image ACM Conferences
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
September 2019
1234 pages
ISBN:9781450368698
DOI:10.1145/3341162
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 September 2019

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Author Tags

  1. extravehicular activity
  2. machine learning
  3. psychophysiology

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  • Research-article

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  • NASA Space Technology Research Fellowship

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UbiComp '19

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2024)Towards a Radiation-Tolerant Display System2024 IEEE Space Computing Conference (SCC)10.1109/SCC61854.2024.00020(122-130)Online publication date: 15-Jul-2024
  • (2022)Importance of Testing with Independent Subjects and Contexts for Machine-Learning Models to Monitor Construction Workers’ Psychophysiological ResponsesJournal of Construction Engineering and Management10.1061/(ASCE)CO.1943-7862.0002341148:9Online publication date: Sep-2022
  • (2020)Assessing Cognitive Performance Using Physiological and Facial FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118114:3(1-41)Online publication date: 4-Sep-2020

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