Estimation of Energy Expenditure and Physical Activity Classification With Wearables

Regular physical activity (PA) is proven to help prevent and treat several non-communicable diseases such as heart disease, stroke, and diabetes. Intensity is a key characteristic of PA that can be assessed by estimating energy expenditure (EE). However, the accuracy of the estimation of EE based on accelerometers are lacking. It has been suggested that the addition of physiological signals can improve the estimation. How much each signal can add to the explained variation and how they can improve the estimation is still unclear. The goal of the current study is twofold: to explore the contribution of heart rate (HR), breathing rate (BR) and skin temperature to the estimation of EE develop and validate a statistical model to estimate EE in simulated free-living conditions based on the relevant physiological signals.