Activity-Aware Deep Cognitive Fatigue Assessment using Wearables
This addresses cognitive fatigue monitoring for workers, an increasing global problem post-COVID-19, but is incremental as it builds on existing wearable sensor methods by adding activity-awareness.
The paper tackles cognitive fatigue assessment by proposing an activity-aware recurrent neural network (AcRoNN) that generalizes individual activity recognition and improves estimation, achieving up to 19% improvement over state-of-the-art methods on datasets from 5 and 27 individuals.
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.