Activity-Aware Deep Cognitive Fatigue Assessment using Wearables

TL;DR

This paper introduces AcRoNN, a novel activity-aware deep learning framework that enhances cognitive fatigue assessment using wearable sensors, addressing individual activity differences for more accurate monitoring.

Contribution

The paper presents a new activity-aware RNN model that generalizes activity recognition and improves cognitive fatigue estimation over existing methods.

Findings

Achieved up to 19% improvement in fatigue estimation accuracy.

Validated on datasets from 5 and 27 individuals.

Demonstrated the importance of activity-awareness in fatigue assessment.

Abstract

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.