Singh Gaganpreet, Chanel Caroline P C, Roy Raphaëlle N
ISAE-SUPAERO, Université de Toulouse, Toulouse, France.
Artificial and Natural Intelligence Toulouse Institute - ANITI, Toulouse, France.
Front Hum Neurosci. 2021 Aug 20;15:692878. doi: 10.3389/fnhum.2021.692878. eCollection 2021.
Manned-Unmanned Teaming (MUM-T) can be defined as the teaming of aerial robots (artificial agents) along with a human pilot (natural agent), in which the human agent is not an authoritative controller but rather a cooperative team player. To our knowledge, no study has yet evaluated the impact of MUM-T scenarios on operators' mental workload (MW) using a neuroergonomic approach (i.e., using physiological measures), nor provided a MW estimation through classification applied on those measures. Moreover, the impact of the non-stationarity of the physiological signal is seldom taken into account in classification pipelines, particularly regarding the validation design. Therefore this study was designed with two goals: (i) to characterize and estimate MW in a MUM-T setting based on physiological signals; (ii) to assess the impact of the validation procedure on classification accuracy. In this context, a search and rescue (S&R) scenario was developed in which 14 participants played the role of a pilot cooperating with three UAVs (Unmanned Aerial Vehicles). Missions were designed to induce high and low MW levels, which were evaluated using self-reported, behavioral and physiological measures (i.e., cerebral, cardiac, and oculomotor features). Supervised classification pipelines based on various combinations of these physiological features were benchmarked, and two validation procedures were compared (i.e., a traditional one that does not take time into account vs. an ecological one that does). The main results are: (i) a significant impact of MW on all measures, (ii) a higher intra-subject classification accuracy (75%) reached using ECG features alone or in combination with EEG and ET ones with the Adaboost, Linear Discriminant Analysis or the Support Vector Machine classifiers. However this was only true with the traditional validation. There was a significant drop in classification accuracy using the ecological one. Interestingly, inter-subject classification with ecological validation (59.8%) surpassed both intra-subject with ecological and inter-subject with traditional validation. These results highlight the need for further developments to perform MW monitoring in such operational contexts.
有人-无人协同（MUM-T）可定义为空中机器人（人工代理）与人类飞行员（自然代理）的协同，其中人类代理并非权威控制者，而是合作团队成员。据我们所知，尚无研究使用神经工效学方法（即利用生理测量）评估MUM-T场景对操作员心理负荷（MW）的影响，也未通过对这些测量进行分类来提供心理负荷估计。此外，生理信号的非平稳性对分类流程的影响很少被考虑，尤其是在验证设计方面。因此，本研究设定了两个目标：（i）基于生理信号在MUM-T环境中表征和估计心理负荷；（ii）评估验证程序对分类准确性的影响。在此背景下，开发了一个搜索救援（S&R）场景，其中14名参与者扮演与三架无人机（无人驾驶飞行器）合作的飞行员角色。任务设计为引发高和低心理负荷水平，并使用自我报告、行为和生理测量（即大脑、心脏和动眼神经特征）进行评估。基于这些生理特征的各种组合的监督分类流程进行了基准测试，并比较了两种验证程序（即不考虑时间的传统程序与考虑时间的生态程序）。主要结果为：（i）心理负荷对所有测量均有显著影响；（ii）单独使用心电图特征或与脑电图和眼电图特征结合，采用Adaboost、线性判别分析或支持向量机分类器时，受试者内分类准确率较高（75%）。然而，这仅在传统验证中成立。使用生态验证时，分类准确率显著下降。有趣的是，生态验证的受试者间分类（59.8%）超过了生态验证的受试者内分类以及传统验证的受试者间分类。这些结果凸显了在这种操作环境中进行心理负荷监测需要进一步发展。
