Distributed logging and synchronization of physiological and performance measures to support adaptive automation strategies

Abstract

As advances in physiological sensors make them more minimally intrusive and easier to use, there is a clear desire by researchers in the fields of Augmented Cognition and Neuroergonomics to incorporate them as much as possible. To best support use of multiple measures, the data from each sensor must be accurately synchronized across all devices and tied to performance and environment events. However, each sensor provides different sampling frequencies, local timing information, and timing accuracy making data synchronization in logs or real time systems difficult. In this paper, a modular architecture is presented to address the issue of how to synchronize data to support analysis of physiological and performance measures. Specific design requirements are presented to ensure the ability to accurately measure raw sensor data and compute metrics in a distributed computing environment to support adaptive automation strategies in a research environment. Finally, an example system is described which combines multiple minimally invasive physiological sensors. © 2011 Springer-Verlag.