Using Smartphone Sensors for Improving Energy Expenditure Estimation

IEEE Journal of Translational Engineering in Health and Medicine

Amit Pande¹  Aveek K. Das¹  Jindan Zhu¹  Yunze Zeng¹  Prasant Mohapatra¹  Jay J. Han² 

[1] Department of Computer Science, University of California at Davis, Davis, CA, USA;Department of Physical Medicine and Rehabilitation, University of California Davis Medical Center, Sacramento, CA, USA;

关键词: Accelerometer;    Barometer;    Energy Expenditure;    Machine Learning;   

DOI  :  10.1109/JTEHM.2015.2480082

来源: DOAJ

【 摘 要 】

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 the most existing schemes work offline or use heuristics. In this paper, 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 EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.

【 授权许可】

Unknown