Identifying human-machine interaction problems in continuous state data

Arik Quang V. Dao, James R. Parkinson, Steven J. Landry

Research output: Contribution to journalConference articlepeer-review


A set of studies has been focused on identifying "markers" in aircraft data that are indicative of human factors issues. In this paper we discuss an experiment that investigated if human error is predictable from the error observed from the combined human-machine system. Sixteen pilots flew simulated instrument approaches under varying levels of workload and control augmentation conditions. Operator control lag, gain, delay, and error extent were computed from aircraft lateral path errors. These parameters along with pupil diameter data were analyzed for differences across workload conditions. Main effects for workload were found with respect to all control parameters consistent with the experiment hypotheses, but the effects were very small. Operator delay in responding to errors appeared inversely correlated with workload. Statistically significant differences were also found with respect to error extent ad pupil diameter.

Original languageEnglish (US)
Pages (from-to)86-90
Number of pages5
JournalProceedings of the Human Factors and Ergonomics Society
StatePublished - 2016
EventHuman Factors and Ergonomics Society 2016 International Annual Meeting, HFES 2016 - Washington, United States
Duration: Sep 19 2016Sep 23 2016

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics


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