Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors

Abstract

Physical inactivity is responsible for 7–10% of all premature deaths worldwide. Thus, valid, reliable and unobtrusive methods for monitoring activities of daily living (ADL) to predict total energy expenditure (TEE) is desired. Multiple methods exist to quantify TEE, but microelectromechanical systems (MEMSs) are the only method, which has shown promising results and are applicable for long-term monitoring in the field. However, no perfect method exists for predicting TEE on a daily basis. The present study evaluates TEE estimation based on a MEMS (Xsens Link system) taking gender and heart rate into account. Fifteen individuals performed seven ADL wearing the Xsens Link system, a heart rate belt and an oxygen mask. Multiple linear regression models were established for sedentary and dynamic activities and evaluated by leave-one-out cross-validation and compared with indirect calorimetry. The linear regression model showed better prediction for dynamic activities (adjusted R2 0.95±0.16) compared to sedentary activities (adjusted R2 0.61±0.19). The root-mean-square error for the TEE estimation ranged between 0.02 and 0.08 kJ/min/kg for the sedentary and dynamic models, respectively. The study showed a viable approach to predict TEE in ADL compared to previously published results. Further studies are warranted to reduce the number of sensors in the estimation of TEE.