Once enabled, these devices continuously monitored heart rate, skin temperature, and locomotion patterns. But given that there are no widely accepted, universal biomarker metrics for fatigue, researchers relied on the wearer’s self-reported perceived exertion levels on a 0-10 scale that they then entered into a machine learning model. Once trained, this model was then used to predict a user’s fatigue levels in real-time to provide a “more nuanced understanding of the subject’s physical state” than past studies, according to researchers.