Pilot workload assessment under different levels of autopilot failure

One of the most interesting topics in the field of human machine interaction is workload. In this paper, using information theory concepts, baud rates generated in all subsystems of a generic simulator of piloting tasks were calculated and then, a unique numerical index presenting an estimation of overall workload was extracted. To examine the effectiveness of offered criteria, three tests with different levels of autopilot failure were designed in which existing workload were labeled based on involving baud rates. A group of subjects performed these tests as the pilots while recording their own idea about perceived workload. Results confirmed that there were statistically significant differences between the averages of scores assigned by subjects to the overall workload for three levels of difficulty. Consequently, the proposed quantitative index is effective enough for determination of workload levels in the simulator environment and facilitates creation of needed scenario noticeably.

[1]  Benjamin Brooks,et al.  Measuring mental workload and physiological reactions in marine pilots: Building bridges towards redlines of performance. , 2018, Applied ergonomics.

[2]  P. Fitts The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[3]  Asok Ray,et al.  Symbolic analysis-based reduced order Markov modeling of time series data , 2017, Signal Process..

[4]  Andy P. Field,et al.  Discovering Statistics Using Ibm Spss Statistics , 2017 .

[5]  Timothy G. Rosser,et al.  Pilot performance comparison between electronic and paper instrument approach charts , 2018 .

[6]  W. E. Hick Quarterly Journal of Experimental Psychology , 1948, Nature.

[7]  Chang Soo Nam,et al.  Quantitative modeling of user performance in multitasking environments , 2018, Comput. Hum. Behav..

[8]  Aerial Camden,et al.  Theoretical Throughput Capacity: Capabilities of Human Information Processing during Multitasking , 2015 .

[9]  Craig M. Walters Application of the Human-Machine Interaction Model to Multiple Attribute Task Battery (MATB): Task Component Interaction and the Strategy Paradigm , 2012 .

[10]  Nadine Matton,et al.  Using theta and alpha band power to assess cognitive workload in multitasking environments. , 2018, International Journal of Psychophysiology.

[11]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[12]  Yamira Santiago-Espada,et al.  The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide , 2011 .

[13]  David I. Schneider An Introduction to Programming Using Visual Basic 2005 , 1996 .

[14]  David I. Schneider An introduction to programming using Visual Basic , 1995 .

[15]  Daniel W. Repperger,et al.  A quantitative model of the human-machine interaction and multi-task performance: A strategy function and the unity model paradigm , 2007, Comput. Biol. Medicine.

[16]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[17]  R. Hyman Stimulus information as a determinant of reaction time. , 1953, Journal of experimental psychology.

[18]  Rohae Myung,et al.  Modeling of multiple sources of workload and time pressure effect with ACT-R , 2017 .

[19]  Eric T. Chancey,et al.  Automation trust and attention allocation in multitasking workspace. , 2018, Applied ergonomics.

[20]  Glenn F. Wilson,et al.  Workload assessment in multi-task environments , 2020 .

[21]  Jugurta R. Montalvão Filho,et al.  Feature selection for optical network design via a new mutual information estimator , 2018, Expert Syst. Appl..

[22]  J. C. Byers,et al.  Comparison of Four Subjective Workload Rating Scales , 1992 .

[23]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[24]  Brett J. Borghetti,et al.  Workload profiles: A continuous measure of mental workload , 2016 .

[25]  Kamran Raissi,et al.  Creating a Numerical Index for Measurement of Workload Levels in the Simulator of Piloting Tasks , 2017 .

[26]  Daniel W. Repperger,et al.  A human–machine interaction strategy function: information throughput and weighting with application to Multiple-Attribute-Task-Battery , 2013 .

[27]  Goutam Sanyal,et al.  Preventing from Cross-VM Side-Channel Attack Using New Replacement Method , 2017, Wireless Personal Communications.

[28]  B. Cain A Review of the Mental Workload Literature , 2007 .

[29]  Robert E. Schlegel,et al.  Operator Functional State Assessment , 2004 .

[30]  Matthew W. Miller,et al.  Empirical evidence for the relationship between cognitive workload and attentional reserve. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[31]  Shalini Kurapati,et al.  Relating Planner Task Performance for Container Terminal Operations to Multi-Tasking Skills and Personality Type , 2017 .

[32]  Sharon Claxton Bommer,et al.  A Theoretical Framework For Evaluating Mental Workload Resources in Human Systems Design for Manufacturing Operations , 2016 .

[33]  Zhe Wang,et al.  Causality Analysis of fMRI Data Based on the Directed Information Theory Framework , 2016, IEEE Transactions on Biomedical Engineering.