Balancing temporal dynamics with measurement noise in real-time situation awareness prediction. Journal Article uri icon

Overview

abstract

  • Situation awareness (SA)-an operator's perception, comprehension, and projection of goal-critical information-is fundamental to the safety and performance of human operators. Recent advances in autonomous systems can reduce operator SA, so researchers have sought real-time, nondisruptive indicators of SA to enable SA-based adaptive cooperation in human-autonomy teams. However, gold-standard freeze-probe measures of SA are not validated for use as ground truth in real-time predictive models. Working memory constraints force single-trial measures to be partial by nature. Existing workarounds smooth over temporal dynamics, precluding real-time predictive models. This work shows that a 3-trial moving average SA score reduces measurement noise while preserving temporal information. Moving average scores are more strongly correlated (r = 0.36, p < 0.01) with performance than single trial SA scores and can be predicted by single-trial physiological signals with greater accuracy (standardized mean absolute error = 0.61, Q2 = 0.36) than single trial SA scores.

publication date

  • September 16, 2025

Date in CU Experts

  • September 17, 2025 10:25 AM

Full Author List

  • Smith KJ; Clark TK; Endsley TC

author count

  • 3

Other Profiles

Electronic International Standard Serial Number (EISSN)

  • 1366-5847

Additional Document Info

start page

  • 1

end page

  • 11