Research Article
Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer
@INPROCEEDINGS{10.1007/978-3-642-37893-5_12, author={Panagiota Anastasopoulou and Sascha H\aa{}rtel and Mirnes Tubic and Stefan Hey}, title={Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer}, proceedings={Wireless Mobile Communication and Healthcare. Third International Conference, MobiHealth 2012, Paris, France, November 21-23, 2012, Revised Selected Papers}, proceedings_a={MOBIHEALTH}, year={2013}, month={4}, keywords={accelerometers barometers energy expenditure physical activity monitoring support vector regression}, doi={10.1007/978-3-642-37893-5_12} }- Panagiota Anastasopoulou
Sascha Härtel
Mirnes Tubic
Stefan Hey
Year: 2013
Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer
MOBIHEALTH
Springer
DOI: 10.1007/978-3-642-37893-5_12
Abstract
Physical inactivity is nowadays defined as the fourth leading risk factor for global mortality. These levels are rising worldwide with major aftereffects on the prevention of several diseases and the general health of the population. Energy expenditure (EE) is a very important parameter usually used as a dimension in physical activity assessment studies. However, the most accurate methods for the measurement of the EE are usually costly, obtrusive and most are limited by laboratory conditions. Recent technological advancements in the sensor technology along with the great progress made in algorithms have made accelerometers a powerful technique often used to assess everyday physical activity. This paper discusses the use of support vector regression (SVR) to predict EE by using a single measurement unit, equipped with a triaxial accelerometer and a barometer, attached to the subject´s hip.