Workload State Classification With Automation During Simulated Air Traffic Control

Abstract

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%,

Comparison of three mobile devices for measuring R–R intervals and heart rate variability: Polar S810i, Suunto t6 and an ambulatory ECG system

“…Instruments like the Polar S810i heart rate monitor are not only used by endurance athletes, but also by scientists for HRV analysis in sport sciences, medicine and other Welds of research, e.g., psychophysiology and ergonomic research (e.g., Kaber et al 2007;Kiviniemi et al 2007;Kumar et al 2007;Turner et al 2004;Wirtz et al 2007;Zhang et al 2007). Recent studies validated the Polar S810i against diVerent ECG systems (Bürklein et al 2005;Gamelin et al 2006;Gamelin et al 2008;Kingsley et al 2005;Vanderlei et al 2008;Nunan et al 2008).…”

Adjustable Autonomy for UAV Supervision Applications Through Mental Workload Assessment Techniques

“…Despite these studies have provided evidences in merging more than one physiological measurements to improve the accuracy of workload classification [13,15], such approaches have proved to be very infeasible from a measurement perspective, affected by the emotional state of the operator and impractical in aircraft cockpits application due to the need of wearing different devices at the same time [16].…”

“…Di Nocera et al in [17] have investigate operator's workload evaluation engaged in simulated flight employing the eye fixations measure and NASA-TLX questionnaire as assessment methodology and Nearest Neighbor algorithm (NN) as classification method. In [16], 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.…”

Comparison of Heart Rate Variability Assessment During Exercise from Polar RS800 and ECG

“…These heart rate monitors have been used by scientists for HRV analysis in sport sciences, medicine and other fields of research [1][2][3]. Several studies have validated these devices against different ECG systems, showing promising results at rest [4][5][6].…”

“…Advances in technology have allowed the development of mobile and easy to use heart rate monitors that allow the quantification of inter-beat intervals (RR intervals) and thus the analysis of heart rate variability. Commercial devices (like Polar, Garmin, Tomtom, or Suunto heart rate monitors, among others) appear as a cheaper alternative, usually providing software which allows for an affordable and a user-friendly method to determine short-term HRV outside of the laboratory setting.These heart rate monitors have been used by scientists for HRV analysis in sport sciences, medicine and other fields of research [1][2][3]. Several studies have validated these devices against different ECG systems, showing promising results at rest [4][5][6].…”