IEEE J Biomed Health Inform. 2022 Sep;26(9):4751-4762. doi: 10.1109/JBHI.2022.3186625. Epub 2022 Sep 9.
In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.
在搜索和救援任务中，无人机操作具有挑战性且认知要求高。高认知负荷会影响救援人员的表现，导致灾难性的失败。为了解决这个问题，我们提出了一种用于实时认知负荷监测的机器学习算法，以了解搜索和救援人员是否需要更换，或者是否需要更多资源。我们的多模态认知负荷监测模型结合了从生理信号（如呼吸、心电图、光体积描记图和皮肤温度）中提取的 25 个特征的信息，这些信号是通过非侵入式方式获取的。为了降低信号的个体间和日间变异性，我们探索了不同的特征归一化技术，并引入了一种基于支持向量机的新加权学习方法，适用于特定于个体的优化。在从 34 名志愿者获得的未见过的测试集中，我们提出的特定于个体的模型能够区分低和高认知负荷，使用传统控制器和新一代控制器分别控制无人机模拟器的平均准确率分别为 87.3%和 91.2%。
