M.Sc Thesis

M.Sc StudentShadmi Ofir
SubjectUse of Physiological Parameters Performance
Evaluation in Flight Simulator
DepartmentDepartment of Architecture and Town Planning
Supervisors PROF. Michael Wagner
Full Thesis text - in Hebrew Full thesis text - Hebrew Version


Since the first flight simulator, flight instructor's main task of monitoring and evaluating trainee performance is an integral part of the flight simulator. The instructor's main tool for performance evaluation is his subjective perceptiveness. During the last few decades manufacturers and researchers of flight simulators have begun the search for additional evaluation tools. The first tools developed were objective performance based tools.
However, their development process was hindered by the difficulty of defining what the optimal performance level actually is, and particularly so in combat flights.In the last few years researchers have begun to explore the possibility of adding an objective measurement dimension, by sampling various parameters off the trainee's body during task performance. This measurement of physiological parameters requires extensive research and development of appropriate measurement tools, analysis processes and display systems. The present research examines several means for implementing the aforementioned measurement, for developing tools and methods of analysis and for displaying the measurement results. Following a review of existing measurement tools and parameters, three measurement tools were chosen. The first measure, scientifically known as GSR, examined in numerous studies, measures the electric conductivity in the trainee's palm fingers. The second measure, known as EMG, measures the electric activity in the palm muscle.The third measure, known as FSR, measures the force applied in the steering stick grip.A basic steering task was developed for the experimental task and performed by the participants on a PC screen. Throughout the task, designated software monitored trainees' performance and compared it to the optimal performance criteria, while at the same time the instructor watched the trainee and subjectively evaluated his performance. In this manner, data was obtained for the 3 standard evaluative dimensions for each task, and additionally the trainee was asked to evaluate his own performance. Each of the 25 participants performed 12 tracking tasks of varying levels of difficulty. The results point to an increase in skin conductivity as task difficulty rises. However, the data received from the other measures was less conclusive. Further on, several data analysis and processing methods were examined. The research findings indicate a new possible data analysis method, based on dividing the sample into subgroups and matching the results analysis method to each subgroup. The comparison of trainee and instructor subjective evaluations revealed gaps, supporting the need for new tools for aiding the instructor make more accurate evaluations of the trainee mental workload.