Autorotation: physiological measures of workload

Abstract

The workload assessment to perform a full autorotation on the AS-350 aircraft (Airbus Helicopters) was performed during a Flight Test Campaign with 80 flight hours and 227 data collection procedures, considering 10 pilots with different piloting skill levels, among such military pilots, flight instructors, and test pilots. During the tests, these pilots were subjected to unexpected engine failures, to evaluate the actual reaction time of each pilot, and to test the ability to make a safe landing under the conditions prescribed by the aircraft manufacturer. The testing method used began with unexpected engine failures when only the lead test pilot knew that the engine failure would be simulated. In the sequence, several points of autorotation were performed, from the simplest profile to the most complex. All the procedures have registered the performance parameters and handling qualities of the aircraft, along with the physiological parameters of the pilots. The aircraft was equipped with dedicated instrumentation for in-flight testing and the pilots have been instrumented with an Electroencephalogram (EEG), Electrocardiogram (EKG), Respiration Belt and Galvanic Skin Response (GSR), Eye Tracking and Face Recognition Camera equipment. This instrumentation was employed to determine physiological markers that could determine the pilot workload, quantitatively, reducing the subjectivity of measures that use only qualitative scales of evaluation, such as Handling Qualities Rate (HQR) and Bedford Workload Scale (WL). In this work, only the preliminary results of the analysis obtained by the Galvanic Skin Response markers will be presented. Major potential applications of the results from the present research range from cockpit design guidelines and human-machine interface systems for supporting pilotage such as more effective alarm systems, interactive cockpits, enhancement of active autopilots with semi-automatic flight commands. Besides that, the results and conclusions from this research can also improve processes and methods for the training-based formation of pilots, along with the development of flight simulators with physiological measurements parameters quantification, feeding back data for a piloting performance assessment.