  Fatigue is a broad, multifactorial concept that includes the subjective
perception of reduced physical and mental energy levels. It is also one of the
key factors that strongly affect patients' health-related quality of life. To
date, most fatigue assessment methods were based on self-reporting, which may
suffer from many factors such as recall bias. To address this issue, in this
work, we recorded multi-modal physiological data (including ECG, accelerometer,
skin temperature and respiratory rate, as well as demographic information such
as age, BMI) in free-living environments and developed automated fatigue
assessment models. Specifically, we extracted features from each modality and
employed the random forest-based mixed-effects models, which can take advantage
of the demographic information for improved performance. We conducted
experiments on our collected dataset, and very promising preliminary results
were achieved. Our results suggested ECG played an important role in the
fatigue assessment tasks.
