An integrated platform for processing pilot trainee’s biometric data

Training and simulation technologies in the field of aviation are evolving towards more human centered solutions where promoting an optimal learning experience is given high priority. Considering the limited availability of flight instructors, the cost in teaching a pilot trainee heavily depends on the effectiveness and efficiency of the training. This requires a timely feedback about the trainee’s status in learning. The development of a platform integrated of methods for inferring pilot’s cognitive/affective states during flight simulation can help the instructors gain a better understanding of pilot trainee’s behaviors. In this way, instructors can provide required corrections and adjustments during the training process to fit different trainee’s learning styles and progresses. The success of this project will significantly improve the quality and reduce the cost of pilot training.

Faculty Supervisor:

Yong Zeng;Dongyu Qiu

Student:

Partner:

CAE

Discipline:

Engineering

Sector:

Aerospace; Artificial Intelligence

University:

Concordia University

Program: