Real-time operator workload assessment and state classification may be useful for decisions about when and how to dynamically apply automation to information processing functions in aviation systems. This research examined multiple cognitive workload measures, including secondary task performance and physiological (cardiac) measures, as inputs to a neural network for operator functional state classification during a simulated air traffic control (ATC) task. Twenty-five participants performed a low-fidelity simulation under manual control or 1 of 4 different forms of automation. Traffic volume was either low (3 aircraft) or high (7 aircraft). Participants also performed a secondary (gauge) monitoring task. Results demonstrated significant effects of traffic volume (workload) on aircraft clearances (p < .01) and trajectory conflicts (p < .01), secondary task performance (p < .01), and subjective ratings of task workload (p < .01). The form of ATC automation affected the number of aircraft collisions (p < .05), secondary task performance (p < .01), and heart rate (HR; p < .01). However, heart rate and heart rate variability measures were not sensitive to the traffic manipulation. Neural network models of controller workload (defined in terms of traffic volume) were developed using the secondary task performance and simple heart rate measure as inputs. The best workload classification accuracy using a genetic algorithm (across all forms of ATC automation) was 64%,
In REF , the authors investigated different classes of cognitive workload measures by merging cardiovascular activity and secondary task performance (a performance-based technique), as inputs to an Artificial Neural Network (ANN) for operator cognitive state classification during a simulated air traffic control task.
Workload State Classification With Automation During Simulated Air Traffic Control
The International Journal of Aviation Psychology
Engineering