DocumentCode :
34731
Title :
Predicting Free-Living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labeled Water
Author :
Bouarfa, Loubna ; Atallah, Louis ; Kwasnicki, Richard Mark ; Pettitt, Claire ; Frost, Gordon ; Guang-Zhong Yang
Author_Institution :
Hamlyn Centre, Imperial Coll. London, London, UK
Volume :
61
Issue :
2
fYear :
2014
fDate :
Feb. 2014
Firstpage :
566
Lastpage :
575
Abstract :
Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions. An EE prediction model was first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p = 0.008, and a MAD of 27 kcal/day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.
Keywords :
accelerometers; biomechanics; biomedical measurement; body sensor networks; estimation theory; feature extraction; medical signal processing; physiological models; signal classification; EE prediction model; accurate daily total energy expenditure estimation; assisted weight management; consistent sensor placement; doubly labeled water; estimation method; free-living EE prediction; free-living energy expenditure prediction; health condition assessment; laboratory-derived prediction model; mean absolute deviation; miniaturized ear-worn sensor; physical activities; single ear-worn accelerometer; task metabolic equivalent; tenfold cross validation error; user compliance; wearable sensors; Acceleration; Accelerometers; Atmospheric measurements; Biomedical measurement; Feature extraction; Particle measurements; Predictive models; Energy expenditure (EE); free-living environment; multiclass feature selection; nearest neighbor (NN) regression; physical activity assessment; wearable sensing device;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2013.2284069
Filename :
6616621
Link To Document :
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