# Activity-Aware Deep Cognitive Fatigue Assessment using Wearables

arXiv: https://arxiv.org/abs/2105.02824 · HTML: https://mlsanity.com/paper/2105.02824/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 4 of 4)

- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers](https://mlsanity.com/paper/1902.08068/) (2019)
- [Fatigue Assessment using ECG and Actigraphy Sensors](https://mlsanity.com/paper/2008.02871/) (2020)

## Built on by (most cited first, 1 of 1)

- [PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing](https://mlsanity.com/paper/2211.02622/) (2022)

## Abstract (arXiv)

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% improvement.