Distributed Logging and Synchronization of Physiological and Performance Measures to Support Adaptive Automation Strategies

  • Daniel Barber
  • Irwin Hudson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6780)


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.


Adaptive Automation Closed-Loop Training System Data Synchronization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Barber
    • 1
  • Irwin Hudson
    • 2
  1. 1.Institute for Simulation and Training Applied Cognition and Training in Immersive Virtual Environments LaboratoryUniversity of Central FloridaOrlando
  2. 2.U.S. Army Research LaboratorySFC Paul Ray Smith Simulation & Training Technology Center (STTC)Orlando

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