Cognitive fatigue has been a common problem among workers which has become an increasing global problem since the emergence of COVID-19 as a global pandemic. While existing multi-modal wearable sensors-aided automatic cognitive fatigue monitoring tools have focused on physical and physiological sensors (ECG, PPG, Actigraphy) analytic on specific group of people (say gamers, athletes, construction workers), activity-awareness is utmost importance due to its different responses on physiology in different person. In this paper, we propose a novel framework, Activity-Aware Recurrent Neural Network (\emph{AcRoNN}), that can generalize individual activity recognition and improve cognitive fatigue estimation significantly. We evaluate and compare our proposed method with state-of-art methods using one real-time collected dataset from 5 individuals and another publicly available dataset from 27 individuals achieving max. 19% improvement.

中文翻译：

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使用可穿戴设备进行活动感知的深度认知疲劳评估

自从COVID-19成为全球大流行病以来，认知疲劳一直是工人中的普遍问题，这已成为一个日益严重的全球性问题。现有的多模式可穿戴式传感器辅助自动认知疲劳监测工具专注于对特定人群（例如游戏玩家，运动员，建筑工人）进行分析的物理和生理传感器（ECG，PPG，Actigraphy），但活动意识至关重要由于它对不同人的生理反应不同。在本文中，我们提出了一个新颖的框架，即活动感知循环神经网络（\ emph {AcRoNN}），该框架可以推广个体活动识别并显着改善认知疲劳估计。我们评估并比较了我们提出的方法和最新方法，该方法使用了一个实时收集的来自5个人的数据集，另一个来自27个人的可公开获得的数据集，均达到了最大值。改善19％。