Energy Expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because most existing schemes work offline or use heuristics. In this work, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor greatly increases the accuracy of EE estimation. Using Bagged Regression Trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual Energy Expenditure (EE). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). The newly-developed EE estimation algorithm demonstrated superior accuracy compared to currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
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