Cross-Subject Continuous Analytic Workload Profiling Using Stochastic Discrete Event Simulation
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States
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Operator functional state OFS in remotely piloted aircraft RPA simulations is modeled using electroencephalograph EEG physiological data and continuous analytic workload profiles CAWPs. A framework is proposed that provides solutions to the limitations that stem from lengthy training data collection and labeling techniques associated with generating CAWPs for multiple operatorstrials. The framework focuses on the creation of scalable machine learning models using two generalization methods 1 the stochastic generation of CAWPs and 2 the use of cross-subject physiological training data to calibrate machine learning models. Cross-subject workload models are used to infer OFS on new subjects, reducing the need to collect truth data or train individualized workload models for unseen operators. Additionally, stochastic techniques are used to generate representative workload profiles using a limited number of training observations. Both methods are found to reduce data collection requirements at the cost of machine learning prediction quality. The costs in quality are considered acceptable due to drastic reductions in machine learning model calibration time for future operators.
- Statistics and Probability