Advanced Algorithms for the Prediction of Adverse Cognitive and Behavioral Conditions in Space

This study utilizes Reaction Self-Test (RST) data collected in N=24 astronauts on 6-month International Space Station missions, arguably the largest cognitive dataset ever collected in spaceflight. The main objective is to additionally obtain data on key environmental stressors (i.e., CO2 levels, temperature, noise, and radiation) and combine them with RST data and other operational data. All data will be integrated in one carefully annotated database, which will be delivered to NASA at the end of the project and could be later amended and mined by other researchers. In addition, this project will develop an individualized dynamic prediction model that informs future Psychomotor Vigilance Test (PVT) performance based on environmental data, survey data, prior PVT administrations, and person-specific characteristics using state-of-the-art machine learning techniques such as functional concurrent regressions and neural networks for time series forecasting. At the end of the study, the team will deliver an algorithm to NASA that, for the first time, can predict adverse cognitive conditions in astronauts early and with an unprecedented precision.

This project develops a state-of-the-art predictive algorithm for adverse cognitive conditions. It is unique as it utilizes the arguably largest cognitive data set ever collected in spaceflight. It is innovative as it combines data on key environmental stressors prevalent in spaceflight with other contextual and time series data to predict cognitive performance. The fact that it will be possible for the first time to predict adverse cognitive conditions in astronauts early and with an unprecedented precision demonstrates the high impact of this proposal for both spaceflight and Earth.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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