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，旨在让飞行员学员具备在不同飞行情景下做出正确决策的能力。飞行员培训的有效性在很大程度上取决于教官对培训情况保持详细了解的能力。有了这些信息，教师可以使学生的培训适应他/她的特定需求，以实现培训效益的最大化。这种意识依赖于飞行员受训人员与决策和表现相关的认知(思维)和情感(情感/感觉)状态的量化。 人类的认知/情感状态可以从生物统计数据中推断出来。例如，脑电波和眼球运动可以测量认知状态。脑力劳动可以通过脑电波测量、瞳孔直径、皮肤电导、心脏测量和呼吸频率来估计。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统计数据中推断出来。这一拟议项目的目标是开发生物测定方法，以量化飞行员受训人员在试点培训过程中的认知和情感状态。该提案的可交付成果 该项目将是量化飞行员受训人员认知和情感状态的综合解决方案。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基于模拟器的飞行员培训环境。这一框架可以很容易地扩展到其他复杂但关键的外地应用，如医疗和军事特派团培训。这个拟议的项目团队将包括三所大学：康科迪亚大学、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，Marient和GlobVision，以及一个研究财团：CRIAQ(魁北克航空航天研究和创新联盟)。

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).