Parsimonious Identification of Physiological Indices for Monitoring Cognitive Fatigue

  • Lance J. Myers
  • J. Hunter Downs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


The objective of this study was to identify a parsimonious set of physiological measures that could be used to best predict cognitive fatigue levels. A 37 hour sleep deprivation study was conducted to induce reduced levels of alertness and cognitive impairment as measured by a psychomotor vigilance test. Non-invasive, wearable and ambulatory sensors were used to acquire cardio-respiratory and motion data during the sleep deprivation. Subsequently 23 potential predictors were derived from the raw sensor data. The least absolute shrinkage and selection operator, along with a cross validation strategy was used to create a sparse model and identify a minimum predictor subset that provided the best prediction accuracy. Final predictor selection was found to vary with task and context. Depending on context selected predictors indicated elevated levels of sympathetic nervous system activity, increased restlessness during engaging tasks and increased cardio-respiratory synchronization with increasing cognitive fatigue.


cognitive fatigue heart rate variability feature selection wearable sensors 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lance J. Myers
    • 1
  • J. Hunter Downs
    • 1
  1. 1.Archinoetics, LLC.HonoluluUSA

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