Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Models
This work addresses fatigue assessment for patients by offering an automated alternative to self-reporting, though it appears incremental as it builds on existing methods with new data.
The authors tackled automated fatigue assessment by developing random forest-based mixed-effects models using multi-modal physiological data from free-living environments, achieving promising preliminary results with ECG identified as a key factor.
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.