Demonstration of an Adaptable Toolkit for the Assessment and Augmentation of Performance by Teams in Real Time (ADAPTER)

Bracken1, B., Palmon1, N., Elkin-Frankston1, E., and Cooke2, N.

Presented at the Global Health and Performance Summit, Columbus, OH (May 2016)

Teams of individuals must multitask to perform their own work while maintaining shared attention across their teammates. Experimenters who study teams use advanced methods to understand physiological, neurophysiological, and behavioral correlates of individual and team performance, test and validate models of performance, and develop augmentation strategies. To support experimenters, we have designed and demonstrated an Adaptable Toolkit for the Assessment and Augmentation of Performance by Teams in Real Time (ADAPTER). ADAPTER employs a toolkit-based approach focused on extensibility, in which a flexible architecture enables rapid integration of new technologies for data capture and analysis. ADAPTER’s data fusion tool fuses data across team members and allows experimenters to compare patterns across the team to optimize models of team performance, supporting a comprehensive and holistic characterization of team performance. To help experimenters create and use models that support research on performance and the development of augmentation strategies, ADAPTER provides a range of model editing and analysis methods that help a user create a model from scratch, implement a relevant model from a library, or modify that existing model before implementation. Finally, ADAPTER’s experimenter interface shows selected processed variables as data are acquired, shows the data being fed through the models, includes the option to edit models in real-time, and shows the cognitive and performance assessment of each team member and the team as a whole viewed across the timeline of the entire experiment. This output can be saved for later examination and manipulation to test hypotheses and to generate new ones.

1 Charles River Analytics Inc.
2 Arizona State University

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