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Cognitive fatigue has been a common problem among workers which has become anincreasing global problem since the emergence of COVID-19 as a global pandemic.While existing multi-modal wearable sensors-aided automatic cognitive fatiguemonitoring 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 itsdifferent responses on physiology in different person. In this paper, wepropose a novel framework, Activity-Aware Recurrent Neural Network(\emph{AcRoNN}), that can generalize individual activity recognition andimprove cognitive fatigue estimation significantly. We evaluate and compare ourproposed method with state-of-art methods using one real-time collected datasetfrom 5 individuals and another publicly available dataset from 27 individualsachieving max. 19% improvement.

Activity-Aware Deep Cognitive Fatigue Assessment using Wearables