  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.
