Fatigue Assessment using ECG and Actigraphy Sensors

TL;DR

This paper presents an automated, objective fatigue assessment system using wearable ECG and actigraphy sensors combined with machine learning, addressing limitations of self-reporting methods.

Contribution

It introduces a novel feature selection approach and a self-attention based deep learning model with CSA mechanism for fatigue detection.

Findings

High accuracy in fatigue classification.

Effective feature selection improves interpretability.

Deep learning model outperforms traditional methods.

Abstract

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system's decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.