Biometric approaches to inferring pilot trainee's affective and cognitive states

飞行安全需要有效的飞行员培训，目的是使飞行员学员具备在不同飞行场景下做出正确决策的能力。飞行员训练的有效性在很大程度上取决于教官对训练情况保持详细了解的能力。有了这些信息，教师可以根据学生的具体需求调整培训，以最大限度地提高培训效益。这种意识依赖于与决策和绩效相关的受训飞行员的认知（思维）和情感（情绪/感觉）状态的量化。**人类的认知/情感状态可以从生物特征数据中推断出来。例如，脑电波和眼球运动可以测量认知状态。精神负荷可以通过脑电波测量、瞳孔直径、皮肤电导、心脏测量和呼吸速率来估计。情感状态，如精神压力和其他情绪可以从身体动作、面部表情和其他生物特征数据推断出来。本研究的目的是发展生物识别方法，以量化受训飞行员在训练过程中的认知和情感状态。这个项目的交付成果将是一个综合的解决方案来量化受训飞行员的认知和情感状态。因此，将开发一个新的框架，将先进的生物测量技术和算法从实验室环境引入到基于模拟器的飞行员培训环境中。该框架可以很容易地扩展到其他复杂但关键的现场应用，如医疗和军事任务训练。这个提议的项目团队将包括三所大学：康考迪亚大学、蒙特利尔大学和麦吉尔大学，一个国家研究实验室：加拿大国家研究委员会，三家公司：CAE、Marivent和GlobVision，以及一个研究联盟：加拿大宇航研究与创新联盟。

Flight safety requires effective pilot training, which aims to equip pilot trainees with the capabilities to make correct decisions in different flight scenarios. The effectiveness of pilot training depends largely on an instructor's ability to maintain a detailed awareness of the training situation. Armed with this information, instructors can adapt a student's training to his/her specific needs in order to maximize the training benefits. This awareness relies on the quantification of pilot trainee's cognitive (thinking) and affective (emotion/feeling) states in relation to decision making and performance.**Human cognitive/affective states can be inferred from biometric data. For instance, brain waves and eye movements can measure cognitive states. Mental workload can be estimated from brain wave measurement, pupil diameter, skin conductance, cardiac measures and respiration rate. Affective state such as mental stress and other emotions can be inferred from body movements, facial expressions, and other biometric data. The objective of this proposed project is to develop biometric approaches for the quantification of pilot trainee's cognitive and affective states during the pilot training process. The deliverable from this proposed**project will be an integrated solution to quantify pilot trainee's cognitive and affective states. As a result, a novel framework will be developed to bring advanced biometric measurement technologies and algorithms from the laboratory setting into the simulator-based pilot training environment. This framework can be easily extended to other complex yet critical field applications such as medical and military mission training. This proposed project team will include three universities: Concordia University, University of Montreal, and McGill University, an national research lab: National Research Council Canada, three companies: CAE, Marivent, and GlobVision, and one research consortium: CRIAQ (Consortium for Research and Innovation in Aerospace in Québec).