Identifying human-machine interaction problems in continuous state data
Arik Quang V. Dao, James R. Parkinson, Steven J. Landry
Marcus Department of Industrial and Manufacturing Engineering
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.
Proceedings of the Human Factors and Ergonomics Society
Human Factors and Ergonomics Society 2016 International Annual Meeting, HFES 2016 - Washington, United States
Duration: Sep 19 2016 → Sep 23 2016
Dive into the research topics of 'Identifying human-machine interaction problems in continuous state data'. Together they form a unique fingerprint.
workload Social Sciences 100%
aircraft Social Sciences 47%
pupil Social Sciences 39%
human error Social Sciences 33%
Aircraft Engineering & Materials Science 29%
experiment Social Sciences 29%
Man machine systems Engineering & Materials Science 28%
Dao, A. Q. V., Parkinson, J. R., & Landry, S. J. (2016). Identifying human-machine interaction problems in continuous state data. Proceedings of the Human Factors and Ergonomics Society, 86-90. https://doi.org/10.1177/1541931213601019
Dao, Arik Quang V. ; Parkinson, James R. ; Landry, Steven J. / Identifying human-machine interaction problems in continuous state data. In: Proceedings of the Human Factors and Ergonomics Society. 2016 ; pp. 86-90.
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title = "Identifying human-machine interaction problems in continuous state data",
abstract = "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.",
author = "Dao, {Arik Quang V.} and Parkinson, {James R.} and Landry, {Steven J.}",
note = "Funding Information: This work was supported by NASA under cooperative agreement NN Publisher Copyright: Copyright 2016 by Human Factors and Ergonomics Society.; Human Factors and Ergonomics Society 2016 International Annual Meeting, HFES 2016 ; Conference date: 19-09-2016 Through 23-09-2016",
journal = "Proceedings of the Human Factors and Ergonomics Society",
Dao, AQV, Parkinson, JR & Landry, SJ 2016, 'Identifying human-machine interaction problems in continuous state data', Proceedings of the Human Factors and Ergonomics Society, pp. 86-90. https://doi.org/10.1177/1541931213601019
Identifying human-machine interaction problems in continuous state data. / Dao, Arik Quang V.; Parkinson, James R.; Landry, Steven J.
In: Proceedings of the Human Factors and Ergonomics Society, 2016, p. 86-90.
T1 - Identifying human-machine interaction problems in continuous state data
AU - Dao, Arik Quang V.
AU - Parkinson, James R.
AU - Landry, Steven J.
N1 - Funding Information: This work was supported by NASA under cooperative agreement NN Publisher Copyright: Copyright 2016 by Human Factors and Ergonomics Society.
N2 - 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.
AB - 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.
JO - Proceedings of the Human Factors and Ergonomics Society
JF - Proceedings of the Human Factors and Ergonomics Society
T2 - Human Factors and Ergonomics Society 2016 International Annual Meeting, HFES 2016
Dao AQV, Parkinson JR, Landry SJ. Identifying human-machine interaction problems in continuous state data. Proceedings of the Human Factors and Ergonomics Society. 2016;86-90. https://doi.org/10.1177/1541931213601019