Daley Matthew S, Gever David, Posada-Quintero Hugo F, Kong Youngsun, Chon Ki, Bolkhovsky Jeffrey B
Naval Submarine Medical Research Laboratory, Groton, CT, United States.
Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
Front Artif Intell. 2020 Apr 7;3:17. doi: 10.3389/frai.2020.00017. eCollection 2020.
High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is "normal" or "impaired" with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.
高风险职业，如飞行员、警察和运输安全管理局特工，需要长时间持续保持警惕，且/或在睡眠极少的情况下工作。这可能导致职业任务中的表现受损。在表现下降显现之前预测受损状态对于防止代价高昂且具有破坏性的错误至关重要。我们假设，为分析眼睛和面部追踪技术指标而开发的机器学习模型能够准确预测受损状态。为了验证这一点，我们使用五种特征选择方法，结合眼睛和面部追踪指标，训练了12种机器学习算法，以预测个体在一项心理运动警觉任务中的表现，该任务在25小时睡眠剥夺方案期间以2小时间隔完成。我们的结果表明：（1）眼睛和面部追踪指标对与受损相关的生理和行为变化敏感；（2）特征选择方法对机器学习算法的分类性能有重大影响；（3）使用眼睛和面部追踪指标的机器学习模型能够正确预测个体表现是“正常”还是“受损”，准确率高达81.6%。这些方法可用于开发基于机器学习的系统，通过使用眼睛和面部追踪指标预测操作员受损情况，以防止因睡眠剥夺导致的操作失误。
