Currently, there are no comprehensive standards for measuring, monitoring, and evaluating task performance with regard to crewmember capabilities, the design of the task, and the dynamic spacecraft environment. This study seeks to address this missing performance infrastructure by providing a conceptual framework for measuring task design quality and developing a path for validation using a task performance metric through experimentation both in university labs and using NASA’s space flight analog missions.
This study has the following objectives:
Using physiological, cognitive, and psychological tracking tools investigators will determine what capabilities they can measure appropriately with non-invasive techniques. Additionally, as part of this goal, they will use the framework to determine whether commercial off-the-shelf (COTS) measurement systems are sufficient to collect the necessary physiological data. They will select the best-suited COTS hardware and design their studies within the constraints of what is available.
This human research portion of the study is divided into stages. Investigators plan to perform a repeated-measures within-subject testing regime to identify whether they can use physiological data to identify the status of crewmember’s capability and how it is altered due to specific tasks. Subjects will undergo three Stages: 1) Baseline Physiological Data Collection, 2) Simulator Training, and 3) Simulator Experiment. The stages will be as close to astronaut-like-subjects as possible; healthy 25-55 year olds with prescreening and no previous cardiac or known neurological conditions.
Baseline Physiological Data Collection The first step for this objective is to determine a baseline physiological signature of each participant. Initial technology development and proof of concept will involve measuring, in a controlled lab setting, a subset of physiological parameters such as pulse, electrodermal activity, and body movement while engaged in normal workplace activities (computer use, eating, and locomotion). Although the main physiological parameters to be measured will be determined from known patterns of functional behavior during these activities, the investigators will broaden their scope and assess other measures such as psychological well-being via a questionnaire and cognition to assess short-term memory, spatial awareness, etc. Participants will be asked to practice the lunar landing simulation for an hour or until they have a series of five consecutive trials with landing accuracy of greater than 90%. If the participant never achieves this criterion after one hour, the participant will be removed from the list.
Simulator Training The participant will be asked to return a few days after baseline physiological data collection, but this time they will be trained to use the lunar landing simulator inside the University of Colorado’s Bioastronautics Lab. The subject will be placed in the moderate-fidelity mock-up of Sierra Nevada Corporations Dreamchaser vehicle. The use of the vehicle is meant to provide a more realistic environment for the participant to be engaged in, as well as increase the fidelity of the scenario to better match a potential space flight mission. The participant will be provided a written description of the expected lunar landing task as well as given oral instructions on how to use the simulator.
Stimulator Experiments For this experiment stage, investigators will introduce the subjects to a series of highly-loaded tasks specifically meant to look at the sensitivity of these measures to reductions in capability. To make this subset of extreme loading tasks, it is noted that tasks can be broken into three categories: physical, cognitive, and psychological. The goal is to pre-load the participants with these extreme physical, cognitive, or psychological exertion and see if this state can be captured by the investigators selected biometrics. There will be three sets of scenarios for these studies: one in which each participant gets a randomized set of pre-loading conditions, another set is where the participant is subjected to one type of pre-loading task for all three days, and the last is where each participant is not subjected to any pre-loading task. The first set will help investigators identify any ordering effects to know if the first preloading causes more of a potent impact than the last pre-loading task. The second set will help them identify potential learning or habituation to the pre-loading. The third set of control data will help to identify any learning effects for those without any pre-loading.
This study is in progress. Results will be available at a later date.
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