Human Energy Expenditure Models: Beyond State-of-the-Art Commercialized Embedded Algorithms

Abstract

In the present study, we propose three new energy expenditure (EE) methods and evaluate their accuracy against state-of-the-art EE estimation commercialized devices. To this end, we used several sensors on 8 subjects to simultaneously record acceleration forces from wrist-located sensors and biopotentials estimated from chest-located ECG devices. These subjects followed a protocol that included a wide range of intensities in a given set of activities, ranging from sedentary to vigorous. The results of the proposed human EE models were compared to indirect calorimetry EE estimated values (kcal/kg/h). The speed-based, heart rate-based and hybrid-based models are characterized by an RMSE of 1.22 ± 0.34 kcal/min, 1.53 ± 0.48 kcal/min and 1.03 ± 0.35 kcal/min, respectively. Based on the presented results, the proposed models provide a significant improvement over the state-of-the-art.