Accurate Energy Expenditure Estimation Using Smartphone Sensors. Pande, A, Zeng, Y, Das, A, Mohapatra, P, Miyamoto, S, Seto, E, Henricson, E., & Han, J. In ACM Wireless Health 2013, pages -, 2013. abstract bibtex 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 of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency and Artificial Neural Networks, a MachineLearning technique to build a generic regression model for EEE that yields upto 89% 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).
@inproceedings{ wh13,
title = {Accurate Energy Expenditure Estimation Using Smartphone Sensors},
author = {A Pande and Y Zeng and A Das and P Mohapatra and S Miyamoto and E Seto and E.K. Henricson and J.J. Han},
booktitle = {ACM Wireless Health 2013},
pages = {-},
year = {2013},
keywords = {MobileHealthcare,MachineLearning},
abstract = { 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 of a common smartphone user. We used existing
smartphone sensors (accelerometer and barometer sensor),
sampled at low frequency and
Artificial Neural Networks, a MachineLearning technique to
build a generic regression model for EEE that yields upto
89% 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).}
}
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