Abstract
In this paper, patterns available in human performance during multitasking supervision of a flight simulator are recognized using probabilistic tools. For this purpose, information produced in the system in the form of input baud rate on the one hand, and the information generated by human as the output baud rate on the other hand, are estimated applying the information theory. In the next stage, two quantities are extracted to discover various features related to the human performance in different operational conditions. Finally, these features are provided as the inputs of the Gaussian mixture model (GMM) and in this way, existing separable categories of human performance are distinguished. To validate the applicability of proposed approach, some experimental tests were conducted in which several individuals performed predefined scenarios with varying degrees of autopilot failure in a standard simulator of pilot’s tasks called Multi-Attribute Task Battery. According to the analysis performed on the experimental data, it can be substantiated that when there was a light failure in the autopilot system, subjects maintained the performance in a stable state by trying to increase the output baud rate; however, if the failure was severe, significant performance degradation was observed although the output baud rate caused by subjects increased meaningfully. Moreover, investigation of GMM output in classification of data revealed that this model was able to recognize the distinct functional regions available in the multitasking performance of human with high accuracy. This accuracy has between-days stability, i.e. it does not vary between GMMs that each one trained by data gathered in a specific day. Also, our investigations show that if all data recorded in different days are used to create a GMM, the resulted model has the same level of accuracy.
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Acknowledgements
The authors would like to gratefully thank the team of researchers in NASA who developed the Multi-Attribute Task Battery (MATB-II), i.e. the standard simulator of piloting tasks used in this study. Moreover, skilled students of aerospace engineering at Amirkabir University of Technology who voluntarily participated as subjects in the experimental tests should be acknowledged.
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Mortazavi, M.R., Raissi, K. & Mehne, S.H.H. A probabilistic approach to classification of human performance during interaction with a standard flight tasks simulator. J Ambient Intell Human Comput 10, 3211–3230 (2019). https://doi.org/10.1007/s12652-018-1038-2
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DOI: https://doi.org/10.1007/s12652-018-1038-2