Fatigue Assessment using ECG and Actigraphy Sensors
This work addresses fatigue assessment for workers and individuals in free-living environments, offering an objective alternative to self-reporting methods, though it appears incremental in its approach.
The paper tackled the problem of fatigue assessment by developing an automated system using ECG and actigraphy sensors with machine learning, achieving promising results through interpretable feature selection and a novel consistency self-attention mechanism.
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.