Activity-Aware Deep Cognitive Fatigue Assessment using Wearables
release_csqlheap3jfo7clz7qqovoazna
by
Mohammad Arif Ul Alam
2021
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
(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
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