A Pande, Y Zeng, A Das, P Mohapatra, S Miyamoto, E Seto, E.K. Henricson, and J.J. Han (2013)
Energy Expenditure Estimation with Smartphone Body Sensors
In: 8th International Conference on Body Area Networks (Bodynets) 2013, pp. −.
Energy Expenditure Estimation is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardio-vascular diseases. Accurate and online EEE utilizing small wearable sensors is a difficult task with most existing schemes working offline or using heuristics. In this work, we focus on accurate EEE for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE.We used Artificial Neural Networks, a machine learning technique to build a generic regression model for EEE that yields upto 89%correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). We were able to demonstrate the superior accuracy achieved by our algorithm. The results were calibrated against COSMED K4b2 calorimeter readings.