Teams of individuals working together toward a common goal must be skilled at multi-tasking to perform their own work while maintaining shared attention across the team. Experimenters who study team performance can use cutting edge methods to assess physiological, neurophysiological, and behavioral underpinnings of optimal performance; however, this requires an adequate understanding of how these signals correlate with individual and team performance. We designed a toolkit to support experimenters in evaluating individual and team performance in a laboratory setting, in testing and validating models of performance, and in developing and validating augmentation strategies to improve performance. Our toolkit provides a framework that flexibly integrates current and emerging sensors. The data fusion tool fuses timesynchronized sensor data to assess performance. The model-building and execution toolset enables experimenters to choose previously entered models, adapt these models according to the current experiment, or develop new models to test. The real-time assessment tool enables experimenters to monitor the state of individual subjects and the team as a whole (e.g., stress, workload, focused attention) throughout the experiment, and how these states relate to performance. This information is then used by the real-time augmentation tool, which suggests augmentations to optimize that performance. Together, these tools provide a proof-of-concept prototype of a flexible modeling tool that would allow sensor inputs to be used to model and predict both individual and team performance.

¹ Charles River Analytics
² Arizona State University