DocumentCode :
3768769
Title :
Using Deep Learning for Energy Expenditure Estimation with wearable sensors
Author :
Jindan Zhu;Amit Pande;Prasant Mohapatra;Jay J. Han
Author_Institution :
Department of Computer Science, University of California at Davis, 95616, United States
fYear :
2015
Firstpage :
501
Lastpage :
506
Abstract :
Energy Expenditure (EE) Estimation is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EE estimation using small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of individuals wearing mobile sensors. We use Convolution Neural Networks (CNNs) to automatically detect important features from data collected from triaxial accelerometer and heart rate sensors. Using CNNs, we find a significant improvement in EE estimation compared to other state-of-the-art models. We compare our results against state-of-the-art Activity-Specific Linear Regression as well as Artificial Neural Networks (ANN) based models. Using a universal CNN model, we obtain an overall low Root Mean Square Error (RMSE) of 1.12 which is 30% and 35% lower than existing models. The results were calibrated against a COSMED K4b2 indirect calorimeter readings.
Keywords :
"Feature extraction","Neural networks","Machine learning","Accelerometers","Sensors","Heart rate","Estimation"
Publisher :
ieee
Conference_Titel :
E-health Networking, Application & Services (HealthCom), 2015 17th International Conference on
Type :
conf
DOI :
10.1109/HealthCom.2015.7454554
Filename :
7454554
Link To Document :
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