IMPRINT was developed for the Army Research Laboratory, and is available, free of charge, to U.S. government agencies. IMPRINT includes algorithms to study performance shaping factors such as fatigue, training, and use of protective clothing with human performance models. IMPRINT includes a sophisticated model of operator workload based on multiple resource theory.

S-PRINT was developed based on extensive literature reviews and meta-analyses on fatigue, automation failure response, and workload overload. We systematically evaluated human-in-the-loop research to identify and quantify factors in long-term space missions that affect astronaut workload, fatigue, and performance. The results of these meta-analyses were used to update IMPRINT algorithms so they more accurately reflect space-specific conditions. In particular, the algorithms contain empirically validated models of sleep-related fatigue, automation failure management, and multitasking in workload overload situations. Further, the team conducted a series of focused human-in-the-loop studies to address specific components of performance that were not answered in the meta-analyses. The S-PRINT tool was developed so it can be used to evaluate performance in missions that are being planned or missions that are currently underway.

The team identified two scenarios of interest for a prototype application of the tool. The primary scenario includes a single astronaut manually controlling a robotic arm when a failure occurs in the environmental process control system. When the astronaut notices the process control failure, s/he needs to prioritize among the different tasks. We worked with NASA subject matter experts (SMEs) to develop human performance models that reflect those situations. The tasks combined in this specific type of situation were of sufficient complexity to be beyond the scope of previously existing models. The team conducted an empirical, human-in-the-loop validation study of the robotic and process control system tasks. A second scenario, involving fire detection and suppression systems, was also implemented and included with the S-PRINT tool. Our scenario development and research efforts focused specifically on worst-case situations: rapid workload transitions (e.g., automation failures, other off-nominal events) resulting in overload, with a single astronaut.

The S-PRINT tool offers users access to the underlying IMPRINT modeling environment. Users who are familiar with human performance modeling can build their own, customized scenarios, and are not limited to the two scenarios developed for this project. S-PRINT also provides an easy-to-use interface in the form of data entry screens that guide the user through the process of building a scenario. It allows the researchers who are not modeling experts to specify numerous relevant factors, e.g., operators, tasks, automation support, use of protective clothing, and sleep history. The output of the model run (for both customized IMPRINT models and S-PRINT-specific models) includes parameters of interest such as operator workload, fatigue effects on task completion time and task accuracy, time to initiate tasks, time to complete tasks, results of task failures, and overall mission times, which can be used to compare relative success.

The effort to provide a new, integrative framework proved highly successful. An empirical validation study of visual attention allocation predicted by the task overload model revealed high correlations (r > 0.95) with actual human performance. This research provided a validated model-based tool to help NASA researchers evaluate potential long-duration missions, identify vulnerabilities, and test potential mitigation strategies to help ensure effective performance and safe space missions. The tool and associated scientific advances offer important insights both for future space scenarios, and for a wide range of other real-world situations.

The project resulted in numerous (19, as of March 2015) publications in peer-reviewed professional journals and conference proceedings. These publications describe different aspects of the human performance research conducted under the grant. In particular, two extensive meta-analyses were conducted to examine 1) the effects of sleep-related fatigue on complex task performance, and 2) the effects of task factors on task management, task switching and task shedding in overload situations. These analyses provided an empirically-derived basis for algorithms that were developed and implemented in the Improved Performance Research Integration Tool (IMPRINT) human performance modeling environment and, in particular, response to unexpected automation failures.

In addition to the meta-analyses, the project included multiple human-in-the-loop (HITL) studies to investigate 1) human-automation interaction (exploring, in particular, the effects of automation design factors and failure types on automation bias and complacency), 2) multitasking in overload situations. The results of these studies have been published and contribute to the scientific knowledge in human-automation interaction and human performance in overload. These two topic areas are relevant in numerous Earth-based domains.

The second major contribution of the S-PRINT project was the development of component models to predict 1) the effects of fatigue (i.e., due to sleep deprivation, sleep restriction, sleep inertia, and circadian cycle effects) on task completion time and task accuracy, 2) the effects of automation design factors (e.g., reliability, degree of automation, or function allocation) on operator performance, 3) the effects of failure type on operator performance, and 4) the effects of task factors (i.e., salience, expectancy, effort, and value) on task selection in overload. These models have been implemented in IMPRINT. All of these areas are relevant in Earth-based industries that require around-the-clock operations, involve the use of automation, and offer the potential for situations that put an operator in overload conditions. Examples include medicine, process control, military operations, and transportation.

Alion Science and Technology, together with Dr. Christopher Wickens, Colorado State University, and Dr. Thomas Jones, proposed to develop and empirically validate the S-PRINT tool. S-PRINT is based on human-performance models that are accessed through a usable interface. S-PRINT allows users to evaluate the effects of automation system design, operator fatigue, and task factors on predicted performance in automation failure scenarios. The project consists of three main lines of work: 1) literature review and meta-analyses, 2) S-PRINT model and tool development, and 3) empirical data collection and validation studies.

The literature review and meta-analyses were conducted to identify and evaluate factors that affect astronaut performance on long-duration space missions. In our literature review effort, we identified three primary areas of research: 1) fatigue and underload effects on performance, 2) human-automation interaction, including factors such as automation reliability and operator complacency, and 3) overload, multitasking, and operator strategies for performing tasks in these conditions. These three areas were researched in parallel to provide a qualitative understanding of the issues (goal of the literature review), and to provide empirically-based data to inform human performance model development (goal of the meta-analyses).

The review provided sufficient data to develop analytic models for predicting the effects of sleep disruption fatigue on complex task performance, and for developing a preliminary model of task selection in overload conditions. It also revealed a need for targeted empirical research in the areas of human-automation interaction and in task selection in overload.

The S-PRINT model and tool development area included four main subtasks: 1) S-PRINT tool development, 2) human performance model development, 3) implementation of analytic models and performance shaping factors developed from the meta-analyses and targeted experimentation, and 4) beta test evaluation and tool improvements. These were all completed in the project.

The S-PRINT tool was developed and the models were implemented and tested. S-PRINT is included within the Improved Performance Research Integration Tool (IMPRINT), a tool that Alion has developed and maintains for the Army Research Laboratory. IMPRINT allows users to build task network models to predict human performance in complex scenarios. S-PRINT allows users to develop and evaluate scenarios using a particular model of operator performance. S-PRINT, as delivered to NASA in March, 2015, includes two library models, and it also provides the capability for users at NASA to build their own custom models using IMPRINT. S-PRINT provides an easy-to-use interface that allows users to create, run, and compare scenarios using already-existing library models. By changing input parameters regarding astronaut fatigue, automation system design, and task characteristics, S-PRINT users can create literally thousands of scenarios. The output from these scenarios can be compared to identify sleep mitigations, automation design changes, allocation of individuals to tasks, or task factor changes that can be adjusted to provide better performance. In addition, S-PRINT was evaluated at a beta test performed with potential system users at NASA.

We identified two scenarios for the basis of the library models. The primary deliverable in S-PRINT was a long-duration mission scenario that would impose significant mental workload on an astronaut. It is of an astronaut working with a remotely-manipulated robotic arm and monitoring an environmental process control system. A fault occurs in the process control system, and rapidly becomes a high-workload off-nominal event. We developed the model of this scenario using data from NASA trainers, astronauts, and from our robotics and process control simulations. Another model, involving an operator using one of three different types of fire detection systems, was developed for testing the S-PRINT human-automation interaction capabilities and for the beta test.

The third task in the tool and model development area – implementing the analytic models and performance shaping factors – has also been completed. The fatigue meta-analysis provided algorithms that specify performance degradations based on sleep deprivation (hours of continual wakefulness), restricted sleep, circadian cycle effects, and sleep inertia. These algorithms, considerably expanding on existing algorithms (e.g., SAFTE) have been added to IMPRINT.

The human-automation interaction (HAI) literature review provided a robust HAI framework and relative importance measures of different factors such as automation reliability and automation failure salience. However, to parameterize the HAI model further, we conducted targeted research. The data collected from the research allowed us to develop a performance shaping factor that applies a benefit to performance when the scenario includes 1) automation that is implemented at a high degree (where most of the functions are allocated to the automation rather than the operator) and 2) is highly reliable, if the automation is functioning normally. This performance shaping factor applies a penalty (e.g., the time to perform tasks is longer, or the accuracy associated with task completion is degraded) in cases where highly automated, highly reliable systems fail. It also applies a penalty in automation failure situations when a salient failure indication is not provided.

From the meta-analysis of task overload and multitasking that might apply to an unexpected emergency management situation, we developed a model of operator task selection and task shedding in overload. This is the Strategic Task Overload Management (STOM) model. The factors of task difficulty, salience (the presence of a reminder), priority, and engagement all affect the probability that an operator will select a given task and, by extension, neglect others. This model has also been implemented within S-PRINT.

The data gathering and validation studies conducted in this effort included a set of ground-based human-in-the-loop (HITL) studies performed at Colorado State University (CSU), specifically designed to provide data for model development and validation. Experiments were conducted to investigate operator performance in working with automation, and in multitasking conditions. These experiments provided data regarding the effects of automation design on operator performance, and the interaction of automation design with fatigue and the interaction of multitasking with fatigue. Three experiments examined the effects of task factors on operator multi-task performance. In particular, within the long duration mission scenario of multi-tasking between robotic arm control and environmental control, the HITL experiment provided data used to validate the STOM model predictions. Over 95% of the variance in actual task switching behavior within this pair of complex, competing tasks was accounted for within the model. The experimental studies also provided data (e.g., times to complete tasks, probability of failure on a given task, performance distributions on the tasks) that was used to populate the human performance model of an astronaut controlling a robotic arm while also monitoring the environmental systems.

The S-PRINT research project was successfully completed. We developed and tested a model-based tool that includes analytic models, performance shaping factors, and task network models of operator performance in complex human-automation interaction scenarios with workload transitions. The models have been developed and validated using empirical data.

IMPRINT was developed for the Army Research Laboratory, and is available, free of charge, to U.S. government agencies. IMPRINT includes algorithms to study performance shaping factors such as fatigue, training, and use of protective clothing with human performance models that include workload.

S-PRINT is being developed based on extensive literature reviews and meta-analyses, in which the team systematically evaluated human-in-the-loop research to identify and quantify factors in long-term space missions that affect astronaut workload, fatigue, and performance. The results of these meta-analyses are being used to update IMPRINT algorithms so they more accurately reflect space-specific conditions. The S-PRINT tool is being developed so it can be used to evaluate performance in missions that are being planned or missions that are currently underway.

The team has identified a scenario of interest for a prototype application of the tool. The scenario includes an astronaut manually controlling a robotic arm when an unannunciated failure occurs in the environmental process control system. The process control failure results in a cascade of events that eventually cause an annunciated alert. When operators notice the process control failure, they will need to prioritize among the different tasks. We are currently working with subject matter experts (SMEs) to perform task analyses and develop human performance models to reflect those situations. SMEs will review the models and their predictions form an early validation study. The team will also perform an empirical, human-in-the-loop validation study, and the validation results will be used to refine the models. Further, the team is conducting focused human-in-the-loop studies to address specific questions that were not answered in the meta-analyses.

Our scenario development and research efforts focus specifically on situations that result in workload transitions (e.g., automation failures, other off-nominal events), placing the astronauts in potential overload situations. These conditions, when addressed by fatigued astronauts, constitute worst case scenarios and require specific, in-depth investigation. One particular goal of this project is to develop a prototype tool that is both usable and useful for analysts, allowing them to easily modify scenarios and evaluate the effects of different factors on mission performance. This tool will provide data entry screens that guide the user through the process of building a scenario. It will allow the researchers to specify numerous relevant factors, e.g., operators, tasks, equipment, automation support, environmental conditions, and sleep schedules. The output of the model run will include parameters of interest such as perceived workload, fatigue, time to initiate tasks, time to complete tasks, task accuracy, task failures (representing human error), results of task failures, and overall mission success. The objective of this research is to develop a validated model-based tool to help NASA researchers evaluate potential long-duration missions, identify vulnerabilities, and test potential mitigation strategies to help ensure effective performance and safe space missions.

In developing models of operator workload and cognitive performance, we conducted extensive meta-analyses investigating the effects on operator performance of the following factors: 1) fatigue due to sleep deprivation, sleep restriction, circadian cycle, and sleep inertia; 2) human automation interaction, including design factors that affect complacency, detection and diagnosis of faults, and implementation of corrective actions; 3) overload and multitasking. These meta-analyses are being used to develop the S-PRINT plug-in to the IMPRINT human performance modeling tool. Because IMPRINT is a Department of Defense tool, the plug-in developed for it can be used by Government entities to examine human performance in a variety of relevant conditions.

S-PRINT will allow users to evaluate other types of missions (e.g., military, process control, medical, aviation) in which fatigued operators work with complex automation and potentially have to deal with unexpected, high-workload situations. S-PRINT will provide a flexible modeling tool (IMPRINT) with empirically based algorithms to predict operator performance (through S-PRINT). Our models of fatigue, human response to automation failures, and task management during overload are applicable in all of these environments.

In addition to the model development efforts, this research includes a significant component of empirical, human-in-the-loop research. These experimental studies address human-automation interaction, operator multitasking, and performance in unexpected automation failure scenarios. This empirical research will contribute to the state of knowledge in fields such as human-automation interaction and operator performance in complex operations.

The project consists of three main lines of work: 1) literature review and meta-analyses, 2) S-PRINT model and tool development, and 3) empirical data collection and validation studies.

Literature Review and Meta-Analyses

The literature review and meta-analyses were conducted to identify and evaluate factors that affect astronaut performance on long-duration space missions. In our literature review effort, we identified three primary areas of research: 1) fatigue and underload effects on performance, 2) human-automation interaction, including factors such as automation reliability and operator complacency, and 3) overload, multitasking, and operator strategies for performing tasks in these conditions. These three areas were researched in parallel to provide a qualitative understanding of the issues (goal of the literature review), and to provide empirically based data to inform human performance model development (goal of the meta-analyses).

Progress: This task was completed at the end of Year 1. Results were provided in a report delivered to NASA on April 8, 2013: Space Performance Research Integration Tool (S-PRINT): Development and Validation of a Model-Based Tool to Predict, Evaluate and Mitigate Excessive Workload Effects - Year 1 Literature Review and Meta-Analyses Summary Report.

S-PRINT Model and Tool Development

The S-PRINT model and tool development area includes three main subtasks: 1) S-PRINT tool development, 2) human performance model development, and 3) implementation of sub-models, algorithms, and performance shaping factors from the meta-analyses.

Progress: This task is on-going and will continue throughout Year 3. We have developed a plan for the S-PRINT tool design, and are currently developing a prototype version of the tool. S-PRINT will be contained within the Improved Performance Research Integration Tool (IMPRINT), a human performance modeling tool that Alion has developed and maintains for the Army Research Laboratory. IMPRINT allows users to build computational models to predict operator performance in complex scenarios. S-PRINT will allow users to develop and evaluate scenarios using a particular model of operator performance. S-PRINT will provide one default model, but will include the capability for users at NASA to build their own custom models using IMPRINT. S-PRINT provides an easy-to-use interface that allows users to create, run, and compare scenarios using already-existing (upon delivery) IMPRINT models. By changing input parameters regarding the astronaut fatigue situation, automation system design, and task characteristics, S-PRINT users can create literally thousands of scenarios. The output from these scenarios can be compared to identify sleep mitigations, automation design changes, or task factor changes that can be adjusted to provide better performance.

We have identified a scenario for developing the human performance model. This includes an astronaut working with a remotely manipulated robotic arm and monitoring an environmental process control system. A fault occurs in the process control system, and rapidly becomes a high-workload off-nominal event. We are currently in the process of developing the model of the scenario, and collecting data from NASA trainers, astronauts, and from our robotics and process control simulations.

The third task in the tool and model development area – implementing the sub-models, algorithms, and performance shaping factors from the meta-analyses into the modeling tool – is currently ongoing. The fatigue meta-analysis provided algorithms that specify performance degradations based on sleep deprivation (hours of continual wakefulness), restricted sleep (consecutive nights with less than 6 hours sleep/night), circadian cycle effects, and sleep inertia (performance upon immediate awakening). Some of these algorithms have already been included into IMPRINT, and others are being reviewed for inclusion. The second area of the meta-analyses, human-automation interaction, addresses specific questions regarding the effects of different automation design factors on operator performance when the automation unexpectedly fails to operate as anticipated (creating a workload transition). These factors include: reliability of automation (inducing complacency), failure type (e.g., no information provided versus misleading information given), salience of alerts, transparency of the interface (e.g., the extent to which the automation provides support for the operator’s mental model, as opposed to simply providing data that the operator must interpret), degree of automation, and availability of guidance in the interface for supporting operator action implementation. These factors are currently being evaluated in project-specific empirical studies.

From the meta-analysis of task overload and multitasking, we have developed a model of operator task selection and task shedding in overload. Factors such as task difficulty, salience (the presence of a reminder), priority, and engagement all affect the probability that an operator will select or shed a given task. We are currently implementing this model in IMPRINT, and are performing empirical studies to investigate the relative weightings of these factors and the presence and significance of a fifth factor: nearness to task completion. The effects of these task-specific factors on operator task selection, and their interaction with fatigue, are currently being investigated through project-specific empirical studies.

Empirical Data Collection and Validation Studies

The data gathering and validation studies being conducted in this effort are a set of ground-based human-in-the-loop (HITL) studies performed at Colorado State University (CSU), specifically designed to provide data for model development.

Progress: This task is currently ongoing, and will continue through Year 3.

Four experiments have been performed to investigate operator performance in working with automation, and in multitasking conditions. In addition, we are planning three further experiments, to address gaps in the meta-analyses. These experiments will provide data regarding the effects of automation design on operator performance, the effects of task factors on operator multi-task performance, and the interaction of automation design with fatigue and (potentially) the interaction of multitasking with fatigue. The experimental studies are also providing data to populate the human performance model: times to complete tasks, probability of failure on a given task, and performance distributions on the tasks. A larger study will be conducted in Year 3. The data gathered in this study will be used for model validation.

Summary

The S-PRINT research project is proceeding on schedule. We have performed literature reviews and meta-analyses in Year 1, and in Year 2 we have begun tool development, model development, and targeted experimental studies to support model development. In Year 3 we will continue to refine the tool and model, and implement model updates based on the experimental results. We will perform a validation study and update the model accordingly.

IMPRINT was developed for the Army Research Laboratory, and is available, free of charge, to U.S. government agencies. IMPRINT includes algorithms to study performance shaping factors such as fatigue, training, and use of protective clothing with human performance models that include workload. SPACEPRINT will be based on an extensive literature review and meta-analysis, in which the team systematically evaluates human-in-the-loop research and lessons learned to identify and quantify factors in long-term space missions that affect astronaut workload, fatigue, and performance. The result of this meta-analysis will be used to update IMPRINT algorithms so they more accurately reflect space-specific conditions. The tool will be developed so it can be run in a predictive mode, to evaluate performance in missions that are being planned, and so it can run in a live mode, using real-time astronaut inputs on workload, fatigue, and wellness.

The live mode will allow planners to identify potential problems as missions are being performed, and evaluate potential mitigation strategies. The team will identify scenarios of interest, perform task analyses with subject matter experts (SMEs), and develop models to reflect those situations. SMEs will review the models and their predictions an early validation study. The team will also perform an empirical, human-in-the-loop validation study. Results of the validations will be used to refine the models.

Our scenario development and research efforts will focus specifically on situations that result in workload transitions (e.g., automation failures, other off-nominal events), placing the astronauts in potential overload situations. These conditions, when addressed by fatigued astronauts, constitute worst case scenarios and require specific, in-depth investigation. One particular goal of this project is to develop a prototype tool that is both usable and useful for analysts, allowing them to easily modify scenarios and evaluate the effects of different factors on mission performance. This tool will provide data entry screens that guide the user through the process of building a scenario. It will allow the researchers to specify numerous relevant factors, e.g., operators, tasks, equipment, environmental conditions, and sleep schedules. The output of the model run will include parameters of interest such as perceived workload, fatigue, time to initiate tasks, time to complete tasks, task accuracy, task failures (representing human error), results of task failures, and overall mission success.

To help NASA achieve these objectives, Alion Science and Technology, together with Dr. Christopher Wickens, Colorado State University, and Dr. Thomas Jones, proposed to develop and empirically validate the S-PRINT tool. S-PRINT is based on human-performance models, together with a usable interface, that allows system designers and mission planners to evaluate the effects of automation system design and fatigue on anticipated performance in automation failure scenarios. The following paragraphs identify the key project lines of work and summarize the progress to date or (where appropriate) briefly outline the plan for further research.

Literature Review and Meta-Analyses

During the literature review, we have identified factors likely to affect astronaut performance. Within this review, we have maintained focus on a few critical issues: task load (i.e., the number of tasks to be completed, the resources required, and the time pressure associated with those tasks), mental workload (the operator’s perception of the burden associated with these tasks, and his/her residual capacity), the complexity of tasks, and the time available to complete tasks. Fatigue, due to sleep restriction, poor quality sleep, and circadian-rhythm desynchronization (potentially exacerbating fatigue effects) can also impact performance.

In addition, emergency conditions that require operators to take control of automated systems and perform complex trouble-shooting tasks create substantial spikes in workload and are associated with slower responses and poorer operator decisions. This is especially true when operators assume that automation is reliable and do not maintain vigilance regarding system functioning (i.e., instances of operator complacency). Given the significant body of research (e.g., Harrison & Horne, 2000) indicating that operator performance (particularly attention and cognitive processing) degrades in fatigued conditions, it is highly likely that long-term fatigue and related physiological factors will also affect performance, especially when automation failures result in sudden workload spikes. Our particular interest in, and focus on, workload transitions (Huey & Wickens, 1993) is because this represents a “worst case” scenario. Astronauts are highly trained on routine tasks, as well as a wide variety of potential off-nominal conditions. However, the situation of fatigued operators and an unexpected event, such as an emergency or an automation failure, is one where operators are most likely to be vulnerable.

In our literature review effort, we identified three primary areas of research: 1) fatigue and underload effects on performance; 2) human-automation interaction, including factors such as automation reliability and operator complacency; and 3) overload, multitasking, and operator strategies for performing tasks in these conditions. These three areas are being researched in parallel to provide a qualitative understanding of the issues (goal of the literature review), and to provide empirically-based data to inform human performance model development (goal of the meta-analyses).

Progress: This task is currently ongoing, and expected to be completed at the end of Year 1. The S-PRINT research effort will continue for 2 additional years; if further relevant research is identified, we will update our analyses. However, the focus of the Year 2 and Year 3 research will shift to model and tool development, empirical data collection, and model validation.

Model and Tool Development

After the meta-analysis and literature review phase, we will develop a prototype version of the S PRINT performance assessment tool. This includes the data entry and interaction techniques, outputs, and the underlying human performance models. S-PRINT will rely on discrete event simulation, allowing users to model and evaluate novel situations to predict operator and system performance. This type of tool can be used to identify and redesign tasks to mitigate factors that are found to contribute to human error.

Progress: This task is currently ongoing. The first step is to identify a scenario, and gather data to support model development. The scenario will include astronauts interacting with automation (potentially two different types of automation, one requiring active monitoring and controlling and one requiring intermittent monitoring) and an unexpected automation failure in one or both systems. Further, the effects of different fatigue-inducing conditions (e.g., sleep restriction, sleep deprivation, or poor quality sleep) will be modeled and evaluated.

Empirical Data Collection and Validation Studies

The validation studies we propose will be ground-based research performed at Colorado State University (CSU). A Year 2 study will provide data for model development, and the Year 3 study will be used for model validation. One critical issue is that the experiment is closely aligned with the model development effort, so both situations address performance in the same scenarios and using readily comparable measures. It is also necessary that the scenarios are relevant to NASA space operations. Possible scenarios include remote operation (robotic) tasks, or process control monitoring tasks, analogous to an astronaut’s task of monitoring spacecraft system status. Because a key focus of the research is in operator response to off-nominal events (i.e., situations causing sudden and unexpected workload transitions), particularly when the operators are fatigued, our experimental scenarios will include these aspects of performance. We will also evaluate operator interactions with automation, to examine the effects of fatigue on automation-induced complacency.

Progress: This task is currently ongoing. We are planning the Year 2 experiment, with the intent of addressing gaps in the literature review and meta-analyses. Specifically, the intersection of the three areas of research investigated in the literature review – operator performance in the case of unexpected automation failures that result in high workload and require multitasking, with fatigued operators – is one requiring additional research.

IMPRINT was developed for the Army Research Laboratory, and is available, free of charge, to U.S. government agencies. IMPRINT includes algorithms to study performance shaping factors such as fatigue, training, and use of protective clothing with human performance models that include workload. SPACEPRINT will be based on an extensive literature review and meta-analysis, in which the team systematically evaluates human-in-the-loop research and lessons learned to identify and quantify factors in long-term space missions that affect astronaut workload, fatigue, and performance. The result of this meta-analysis will be used to update IMPRINT algorithms so they more accurately reflect space-specific conditions. The tool will be developed so it can be run in a predictive mode, to evaluate performance in missions that are being planned, and so it can run in a live mode, using real-time astronaut inputs on workload, fatigue, and wellness.

The live mode will allow planners to identify potential problems as missions are being performed, and evaluate potential mitigation strategies. The team will identify scenarios of interest, perform task analyses with subject matter experts (SMEs), and develop models to reflect those situations. SMEs will review the models and their predictions an early validation study. The team will also perform an empirical, human-in-the-loop validation study. Results of the validations will be used to refine the models.

Our scenario development and research efforts will focus specifically on situations that result in workload transitions (e.g., automation failures, other off-nominal events), placing the astronauts in potential overload situations. These conditions, when addressed by fatigued astronauts, constitute worst case scenarios and require specific, in-depth investigation. One particular goal of this project is to develop a prototype tool that is both usable and useful for analysts, allowing them to easily modify scenarios and evaluate the effects of different factors on mission performance. This tool will provide data entry screens that guide the user through the process of building a scenario. It will allow the researchers to specify numerous relevant factors, e.g., operators, tasks, equipment, environmental conditions, and sleep schedules. The output of the model run will include parameters of interest such as perceived workload, fatigue, time to initiate tasks, time to complete tasks, task accuracy, task failures (representing human error), results of task failures, and overall mission success.