For teams to perform effectively, individuals must focus on their own tasks, while simultaneously maintaining awareness of other team members. Researchers studying and attempting to optimize performance of teams as well as individual team members use assessments of behavioral, neurophysiological, and physiological signals that correlate with individual and team performance. However, synchronizing data from multiple sensor devices can be difficult, and building and using models to assess human states of interest can be time-consuming and non-intuitive. To assist researchers, we built an Adaptable Toolkit for the Assessment and Augmentation of Performance by Teams in Real Time (ADAPTER), which provides a framework that flexibly integrates sensors and fuses sensor data to assess performance. ADAPTER flexibly integrates current and emerging sensors; assists researchers in creating and implementing models that support research on performance and the development of augmentation strategies; and enables comprehensive and holistic characterization of team member performance during real-time experimental protocols.
