Predicting fatigue from heart rate signatures using functional logistic regression

Abstract

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real‐life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data‐driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7‐mile Grand Canyon rim‐to‐rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real‐life application.