Using Smartphone Sensors for Improving Energy Expenditure Estimation
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 o...
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2015-01-01
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doaj-b11820d70dd84643a12313fef7ba7ae82021-03-29T18:38:52ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722015-01-01311210.1109/JTEHM.2015.24800827272038Using Smartphone Sensors for Improving Energy Expenditure EstimationAmit Pande0Jindan Zhu1Aveek K. Das2Yunze Zeng3Prasant Mohapatra4Jay J. Han5Department of Computer Science, University of California at Davis, Davis, CA, USADepartment of Computer Science, University of California at Davis, Davis, CA, USADepartment of Computer Science, University of California at Davis, Davis, CA, USADepartment of Computer Science, University of California at Davis, Davis, CA, USADepartment of Computer Science, University of California at Davis, Davis, CA, USADepartment of Physical Medicine and Rehabilitation, University of California Davis Medical Center, Sacramento, CA, USAEnergy 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.https://ieeexplore.ieee.org/document/7272038/AccelerometerBarometerEnergy ExpenditureMachine Learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amit Pande Jindan Zhu Aveek K. Das Yunze Zeng Prasant Mohapatra Jay J. Han |
spellingShingle |
Amit Pande Jindan Zhu Aveek K. Das Yunze Zeng Prasant Mohapatra Jay J. Han Using Smartphone Sensors for Improving Energy Expenditure Estimation IEEE Journal of Translational Engineering in Health and Medicine Accelerometer Barometer Energy Expenditure Machine Learning |
author_facet |
Amit Pande Jindan Zhu Aveek K. Das Yunze Zeng Prasant Mohapatra Jay J. Han |
author_sort |
Amit Pande |
title |
Using Smartphone Sensors for Improving Energy Expenditure Estimation |
title_short |
Using Smartphone Sensors for Improving Energy Expenditure Estimation |
title_full |
Using Smartphone Sensors for Improving Energy Expenditure Estimation |
title_fullStr |
Using Smartphone Sensors for Improving Energy Expenditure Estimation |
title_full_unstemmed |
Using Smartphone Sensors for Improving Energy Expenditure Estimation |
title_sort |
using smartphone sensors for improving energy expenditure estimation |
publisher |
IEEE |
series |
IEEE Journal of Translational Engineering in Health and Medicine |
issn |
2168-2372 |
publishDate |
2015-01-01 |
description |
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. |
topic |
Accelerometer Barometer Energy Expenditure Machine Learning |
url |
https://ieeexplore.ieee.org/document/7272038/ |
work_keys_str_mv |
AT amitpande usingsmartphonesensorsforimprovingenergyexpenditureestimation AT jindanzhu usingsmartphonesensorsforimprovingenergyexpenditureestimation AT aveekkdas usingsmartphonesensorsforimprovingenergyexpenditureestimation AT yunzezeng usingsmartphonesensorsforimprovingenergyexpenditureestimation AT prasantmohapatra usingsmartphonesensorsforimprovingenergyexpenditureestimation AT jayjhan usingsmartphonesensorsforimprovingenergyexpenditureestimation |
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1724196750992867328 |