Modeling Biological Rhythms to Predict Mental and Physical Readiness

Abstract

The human body is composed of various biological clocks that impact physical and mental health functioning. Modeling biological rhythms provides the means to understand the effect of internal and external factors on human mental and physical performance. So far, biological rhythms have mostly been studied in controlled laboratory settings thus limiting the long term study and modeling of these rhythms. This paper presents the results of our exploratory study of modeling human rhythms with longitudinal physiological data collected from consumer devices in the wild. We used data from four people continuously wearing Empatica (E4) wristbands and Oura smart rings for approximately four months to build models of human rhythms. We then used those model parameters in a machine learning approach to predict mental and physical readiness. Our results showed that most models built with a combination of sensors and rhythmic features obtained a prediction accuracy above the baseline measure of 66% (Max accuracy = 82.7%). These results provide insights into the feasibility of using consumer devices to model biological rhythms and use them to assess human and performance and health.