Discovering Oculometric Patterns to Detect Cognitive Performance Changes in Healthy Youth Football Athletes

Gaurav N. Pradhan, Jamie M. Bogle, Michael J. Cevette, Jan Stepanek

Research output: Contribution to journalArticlepeer-review


In this paper, we focus on the application of oculometric patterns extracted from raw eye movements during a mental workload task to assess changes in cognitive performance in healthy youth athletes over the course of a typical sport season. Oculometric features pertaining to fixations and saccades were measured on 116 athletes in pre- and post-season testing. Participants were between 7 and 14 years of age at pre-season testing. Due to varied developmental rates, there were large interindividual performance differences during a mental workload task consisting of reading numbers. Based on different reading speeds, we classified three profiles (slow, moderate, and fast) and established their corresponding baselines for oculometric data. Within each profile, we describe changes in oculomotor function based on changes in cognitive performance during the season. To visualize these changes in multidimensional oculometric data, we also present a multidimensional visualization tool named DiViTo (diagnostic visualization tool). These experimental, computational informatics and visualization methodologies may serve to utilize oculometric information to detect changes in cognitive performance due to mild or severe cognitive impairment such as concussion/mild traumatic brain injury, as well as possibly other disorders such as attention deficit hyperactivity disorders, learning/reading disabilities, impairment of alertness, and neurocognitive function.

Original languageEnglish (US)
Pages (from-to)371-392
Number of pages22
JournalJournal of Healthcare Informatics Research
Issue number4
StatePublished - Dec 1 2019


  • Cognitive performance
  • Concussion
  • Eye tracking
  • Multidimensional patterns
  • Oculometrics
  • Pre- and post-season

ASJC Scopus subject areas

  • Information Systems
  • Health Informatics
  • Computer Science Applications
  • Artificial Intelligence


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