TY - GEN
T1 - A prototype toolkit for sensing and modeling individual and team state
AU - Bracken, Bethany K.
AU - Palmon, Noa
AU - Romero, Victoria
AU - Pfautz, Jonathan
AU - Cooke, Nancy
PY - 2014
Y1 - 2014
N2 - 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 time-synchronized 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.
AB - 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 time-synchronized 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.
UR - http://www.scopus.com/inward/record.url?scp=84957694071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84957694071&partnerID=8YFLogxK
U2 - 10.1177/1541931214581199
DO - 10.1177/1541931214581199
M3 - Conference contribution
AN - SCOPUS:84957694071
T3 - Proceedings of the Human Factors and Ergonomics Society
SP - 949
EP - 953
BT - 2014 International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
PB - Human Factors an Ergonomics Society Inc.
T2 - 58th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
Y2 - 27 October 2014 through 31 October 2014
ER -